20 May 2021

Here we’ll use the Bray-Curtis index to identify sample replicates (within each locus) that are more dissimilar than similar to the other replicates. Dissimilarity can be an indication of an issue (contamination, etc.) with a particular replicate.

This uses the output from the species-occupancy detection modeling in 03-species-occupancy-model.Rmd The occupancy modeling uses the ASVs (not taxonomy) and I’ll use a similar approach here with the Bray-Curtis and NMDS analyses.

Process

The process is a bit cumbersome because it requires looking at each sample/locus/replicate for the full reference DNA pool and vouchered reference pool.

The process for looking at the dissimilarity among replicates is to: 1. Read in data that has been cleaned up using the occupancy modeling 2. Create a community matrix (per locus) 3. Standardize data across replicates (fct decostand) 4. Generate Bray-Curtis distances (fct vegdist) 4a. Are any replicates more dissimilar than similar? 5. Generate NMDS plots from distance matrix (fct metaMDS)

  1. Read in list

Outputs

Based on the NMDS plots and Bray-Curtis dissimilarity index, I generated a list of samples to remove:

../data/reference_pool_dissimilarity_samples_to_remove.csv

The data that were used to generate that list are output .csv files from the Bray-Curtis function, implemented below.

In addition, three loci had insufficient data across all 18 samples in both the vouchered and full reference pools to be included in these analyses and will be dropped from further analyses:

16Sfish teleo crust2

source("../R/metabarcoding-funcs.R")
library(tidyverse)
library(stringi)
library(vegan)
library(reshape2)
library(textshape)
library(rlist)


# output from the ASV filtering based on the SODM for 
# vouchered ref
vrp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/voucher_features_sodm_filtered_taxonomy_df.rds")

# full reference
frp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/full_reference_sodm_filtered_taxonomy_df.rds")

I have wrapped the Bray-Curis and NMDS up into a function called bray_nmds_complete which outputs a .csv file with the replicates that are > 0.49 dissimilar and generates an NMDS plot.

I’ll use that function with an lappy and the list of loci, since each locus will be analyzed separately.

To cycle over a list of the loci…

loc_list19
 [1] "16SH1"        "16Svar"       "18SSSU3"      "18Sn4"        "L2513H2714"   "aquaF2"      
 [7] "aquaF3"       "cep"          "ceph16S"      "crust16S"     "fishcoilbc"   "fishminiA"   
[13] "mifish"       "minibar"      "nsCOIFo"      "plankCOI"     "shark474"     "sharkCOImini"
[19] "short28S"    

Full Reference dataframe

# cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
lapply(loc_list19, bray_nmds_complete, sodm_filtered_df = frp_sodm_filtered_df, sample = "FRP")
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero
Run 0 stress 0.02549398 
Run 1 stress 0.06753572 
Run 2 stress 0.04566323 
Run 3 stress 0.02549088 
... New best solution
... Procrustes: rmse 0.001081767  max resid 0.001961006 
... Similar to previous best
Run 4 stress 0.04566343 
Run 5 stress 0.04566312 
Run 6 stress 0.1679505 
Run 7 stress 0.2212383 
Run 8 stress 0.1679505 
Run 9 stress 0.06753611 
Run 10 stress 0.06611416 
Run 11 stress 0.06753527 
Run 12 stress 0.0254977 
... Procrustes: rmse 0.003715123  max resid 0.007017285 
... Similar to previous best
Run 13 stress 0.06753667 
Run 14 stress 0.06611411 
Run 15 stress 0.02549271 
... Procrustes: rmse 0.001928513  max resid 0.003693382 
... Similar to previous best
Run 16 stress 0.1675538 
Run 17 stress 0.06611438 
Run 18 stress 0.04566336 
Run 19 stress 0.02549439 
... Procrustes: rmse 0.00138344  max resid 0.002461619 
... Similar to previous best
Run 20 stress 0.3017353 
Run 21 stress 0.02549458 
... Procrustes: rmse 0.001435473  max resid 0.002553668 
... Similar to previous best
Run 22 stress 0.06753608 
Run 23 stress 0.06611407 
Run 24 stress 0.02549067 
... New best solution
... Procrustes: rmse 0.0002607035  max resid 0.0004446153 
... Similar to previous best
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.06789099 
Run 1 stress 0.06116551 
... New best solution
... Procrustes: rmse 0.1716614  max resid 0.3019298 
Run 2 stress 0.06116556 
... Procrustes: rmse 7.298398e-05  max resid 0.0001426581 
... Similar to previous best
Run 3 stress 0.3209238 
Run 4 stress 0.06116557 
... Procrustes: rmse 3.670787e-05  max resid 6.069345e-05 
... Similar to previous best
Run 5 stress 0.06116551 
... Procrustes: rmse 1.749457e-05  max resid 3.085404e-05 
... Similar to previous best
Run 6 stress 0.08239048 
Run 7 stress 0.06116551 
... Procrustes: rmse 1.75612e-05  max resid 2.877174e-05 
... Similar to previous best
Run 8 stress 0.2043563 
Run 9 stress 0.06789111 
Run 10 stress 0.06116551 
... New best solution
... Procrustes: rmse 9.187778e-06  max resid 1.529488e-05 
... Similar to previous best
Run 11 stress 0.08239083 
Run 12 stress 0.06789132 
Run 13 stress 0.08239047 
Run 14 stress 0.06116551 
... New best solution
... Procrustes: rmse 7.046014e-06  max resid 1.072198e-05 
... Similar to previous best
Run 15 stress 0.06116552 
... Procrustes: rmse 3.771754e-05  max resid 7.569807e-05 
... Similar to previous best
Run 16 stress 0.1681478 
Run 17 stress 0.0678909 
Run 18 stress 0.06789153 
Run 19 stress 0.06116551 
... New best solution
... Procrustes: rmse 1.084345e-05  max resid 1.715373e-05 
... Similar to previous best
Run 20 stress 0.06116551 
... Procrustes: rmse 6.06084e-06  max resid 9.507563e-06 
... Similar to previous best
Run 21 stress 0.06789127 
Run 22 stress 0.06789101 
Run 23 stress 0.06789113 
Run 24 stress 0.06789112 
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 9.579796e-05 
Run 1 stress 9.427029e-05 
... New best solution
... Procrustes: rmse 0.02247895  max resid 0.04396397 
Run 2 stress 8.642301e-05 
... New best solution
... Procrustes: rmse 0.1175535  max resid 0.2402706 
Run 3 stress 8.540527e-05 
... New best solution
... Procrustes: rmse 0.09818878  max resid 0.1935 
Run 4 stress 9.678334e-05 
... Procrustes: rmse 0.08816036  max resid 0.1795636 
Run 5 stress 9.650868e-05 
... Procrustes: rmse 0.03322299  max resid 0.06630169 
Run 6 stress 9.280767e-05 
... Procrustes: rmse 0.001147417  max resid 0.002240407 
... Similar to previous best
Run 7 stress 9.748182e-05 
... Procrustes: rmse 0.0966331  max resid 0.1974143 
Run 8 stress 8.37519e-05 
... New best solution
... Procrustes: rmse 0.1103649  max resid 0.2095578 
Run 9 stress 7.870168e-05 
... New best solution
... Procrustes: rmse 0.03600446  max resid 0.06782502 
Run 10 stress 8.075581e-05 
... Procrustes: rmse 0.03398  max resid 0.0644858 
Run 11 stress 9.517662e-05 
... Procrustes: rmse 0.2313203  max resid 0.4739927 
Run 12 stress 6.63805e-05 
... New best solution
... Procrustes: rmse 0.03534882  max resid 0.06626279 
Run 13 stress 9.683091e-05 
... Procrustes: rmse 0.1111087  max resid 0.2135577 
Run 14 stress 8.881884e-05 
... Procrustes: rmse 0.2341704  max resid 0.474671 
Run 15 stress 8.930062e-05 
... Procrustes: rmse 0.244706  max resid 0.4986702 
Run 16 stress 9.769992e-05 
... Procrustes: rmse 0.04268541  max resid 0.08052105 
Run 17 stress 8.760182e-05 
... Procrustes: rmse 0.069625  max resid 0.1321595 
Run 18 stress 9.781914e-05 
... Procrustes: rmse 0.07685473  max resid 0.1461738 
Run 19 stress 8.628186e-05 
... Procrustes: rmse 0.2465064  max resid 0.502787 
Run 20 stress 9.733444e-05 
... Procrustes: rmse 0.2519811  max resid 0.5153881 
Run 21 stress 9.374369e-05 
... Procrustes: rmse 0.2539169  max resid 0.5198679 
Run 22 stress 9.943772e-05 
... Procrustes: rmse 0.02757721  max resid 0.05187796 
Run 23 stress 9.467886e-05 
... Procrustes: rmse 0.2388507  max resid 0.485292 
Run 24 stress 9.9242e-05 
... Procrustes: rmse 0.0791815  max resid 0.1506837 
Run 25 stress 8.403568e-05 
... Procrustes: rmse 0.03850305  max resid 0.07203395 
Run 26 stress 9.98365e-05 
... Procrustes: rmse 0.1769452  max resid 0.349054 
Run 27 stress 9.877062e-05 
... Procrustes: rmse 0.01073389  max resid 0.0201402 
Run 28 stress 9.577971e-05 
... Procrustes: rmse 0.00574002  max resid 0.01074771 
Run 29 stress 9.874117e-05 
... Procrustes: rmse 0.237319  max resid 0.4818348 
Run 30 stress 9.869722e-05 
... Procrustes: rmse 0.2541792  max resid 0.5204795 
Run 31 stress 9.232483e-05 
... Procrustes: rmse 0.247805  max resid 0.5057687 
Run 32 stress 9.932195e-05 
... Procrustes: rmse 0.2481882  max resid 0.5066865 
Run 33 stress 9.034751e-05 
... Procrustes: rmse 0.157195  max resid 0.307463 
Run 34 stress 7.855983e-05 
... Procrustes: rmse 0.212911  max resid 0.4270818 
Run 35 stress 9.517238e-05 
... Procrustes: rmse 0.09108466  max resid 0.1739436 
Run 36 stress 9.564499e-05 
... Procrustes: rmse 0.2382372  max resid 0.4839096 
Run 37 stress 9.858864e-05 
... Procrustes: rmse 0.248585  max resid 0.5075672 
Run 38 stress 9.447921e-05 
... Procrustes: rmse 0.1255467  max resid 0.2425351 
Run 39 stress 9.114647e-05 
... Procrustes: rmse 0.2509805  max resid 0.5130703 
Run 40 stress 8.99543e-05 
... Procrustes: rmse 0.02864414  max resid 0.05389806 
Run 41 stress 9.885274e-05 
... Procrustes: rmse 0.1563639  max resid 0.3057428 
Run 42 stress 9.716382e-05 
... Procrustes: rmse 0.2539709  max resid 0.5199975 
Run 43 stress 9.892495e-05 
... Procrustes: rmse 0.07448279  max resid 0.1415641 
Run 44 stress 9.385376e-05 
... Procrustes: rmse 0.1123675  max resid 0.2160528 
Run 45 stress 9.566503e-05 
... Procrustes: rmse 0.2492487  max resid 0.5091057 
Run 46 stress 9.824921e-05 
... Procrustes: rmse 0.09186905  max resid 0.1754727 
Run 47 stress 6.942604e-05 
... Procrustes: rmse 0.07190642  max resid 0.1365706 
Run 48 stress 8.646939e-05 
... Procrustes: rmse 0.244192  max resid 0.4974864 
Run 49 stress 8.255958e-05 
... Procrustes: rmse 0.1158942  max resid 0.2231095 
Run 50 stress 9.87686e-05 
... Procrustes: rmse 0.080067  max resid 0.1524091 
Run 51 stress 9.647351e-05 
... Procrustes: rmse 0.2502516  max resid 0.5114115 
Run 52 stress 9.976061e-05 
... Procrustes: rmse 0.243254  max resid 0.4953604 
Run 53 stress 8.374791e-05 
... Procrustes: rmse 0.01420416  max resid 0.02666688 
Run 54 stress 9.085281e-05 
... Procrustes: rmse 0.1429225  max resid 0.2779268 
Run 55 stress 9.928144e-05 
... Procrustes: rmse 0.0346385  max resid 0.06480249 
Run 56 stress 9.685915e-05 
... Procrustes: rmse 0.09805547  max resid 0.1876499 
*** No convergence -- monoMDS stopping criteria:
    56: stress < smin
stress is (nearly) zero: you may have insufficient data

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero

Run 0 stress 0.03982062 
Run 1 stress 0.03982118 
... Procrustes: rmse 0.0005872108  max resid 0.0008446614 
... Similar to previous best
Run 2 stress 0.08670257 
Run 3 stress 0.09990103 
Run 4 stress 0.07941983 
Run 5 stress 0.05583793 
Run 6 stress 0.04111592 
Run 7 stress 0.04111487 
Run 8 stress 0.05583784 
Run 9 stress 0.03982081 
... Procrustes: rmse 0.0003801426  max resid 0.0005443656 
... Similar to previous best
Run 10 stress 0.03982142 
... Procrustes: rmse 0.0006806971  max resid 0.0009800321 
... Similar to previous best
Run 11 stress 0.04111577 
Run 12 stress 0.03982075 
... Procrustes: rmse 0.0003134993  max resid 0.0004214406 
... Similar to previous best
Run 13 stress 0.03982068 
... Procrustes: rmse 0.0002667816  max resid 0.0003818783 
... Similar to previous best
Run 14 stress 0.05700056 
Run 15 stress 0.03982074 
... Procrustes: rmse 0.0002821026  max resid 0.0004022993 
... Similar to previous best
Run 16 stress 0.04111488 
Run 17 stress 0.09992496 
Run 18 stress 0.05583784 
Run 19 stress 0.04111507 
Run 20 stress 0.03982186 
... Procrustes: rmse 0.0008269277  max resid 0.001192183 
... Similar to previous best
Run 21 stress 0.05700057 
Run 22 stress 0.03982156 
... Procrustes: rmse 0.0007155026  max resid 0.00102945 
... Similar to previous best
Run 23 stress 0.09992516 
Run 24 stress 0.09990102 
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero

Run 0 stress 0.08062364 
Run 1 stress 0.08062463 
... Procrustes: rmse 0.001414915  max resid 0.00365458 
... Similar to previous best
Run 2 stress 0.08062483 
... Procrustes: rmse 0.00168243  max resid 0.004292421 
... Similar to previous best
Run 3 stress 0.08062476 
... Procrustes: rmse 0.001613128  max resid 0.004175612 
... Similar to previous best
Run 4 stress 0.08062494 
... Procrustes: rmse 0.001838937  max resid 0.004666044 
... Similar to previous best
Run 5 stress 0.08315292 
Run 6 stress 0.08315048 
Run 7 stress 0.08315198 
Run 8 stress 0.08315018 
Run 9 stress 0.08062457 
... Procrustes: rmse 0.0007818514  max resid 0.001987424 
... Similar to previous best
Run 10 stress 0.1402712 
Run 11 stress 0.08315043 
Run 12 stress 0.08062181 
... New best solution
... Procrustes: rmse 0.002392605  max resid 0.006193983 
... Similar to previous best
Run 13 stress 0.08315058 
Run 14 stress 0.08062424 
... Procrustes: rmse 0.002795577  max resid 0.007395728 
... Similar to previous best
Run 15 stress 0.08315021 
Run 16 stress 0.08315125 
Run 17 stress 0.08315049 
Run 18 stress 0.1284401 
Run 19 stress 0.08315046 
Run 20 stress 0.08062452 
... Procrustes: rmse 0.003649667  max resid 0.009453231 
... Similar to previous best
Run 21 stress 0.08315019 
Run 22 stress 0.1402707 
Run 23 stress 0.08062456 
... Procrustes: rmse 0.003665198  max resid 0.009547438 
... Similar to previous best
Run 24 stress 0.1284401 
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.06013984 
Run 1 stress 0.185523 
Run 2 stress 0.06013986 
... Procrustes: rmse 5.603937e-05  max resid 0.0001059202 
... Similar to previous best
Run 3 stress 0.1201634 
Run 4 stress 0.06013984 
... Procrustes: rmse 6.64174e-06  max resid 1.120539e-05 
... Similar to previous best
Run 5 stress 0.06013984 
... Procrustes: rmse 3.797987e-06  max resid 5.498389e-06 
... Similar to previous best
Run 6 stress 0.06013984 
... Procrustes: rmse 8.835351e-06  max resid 1.397334e-05 
... Similar to previous best
Run 7 stress 0.06013984 
... New best solution
... Procrustes: rmse 1.561536e-06  max resid 1.971981e-06 
... Similar to previous best
Run 8 stress 0.3209238 
Run 9 stress 0.06013984 
... Procrustes: rmse 1.126421e-05  max resid 2.023166e-05 
... Similar to previous best
Run 10 stress 0.1191491 
Run 11 stress 0.06013984 
... Procrustes: rmse 2.768543e-06  max resid 5.679992e-06 
... Similar to previous best
Run 12 stress 0.07031996 
Run 13 stress 0.06013985 
... Procrustes: rmse 2.459416e-05  max resid 4.161665e-05 
... Similar to previous best
Run 14 stress 0.06013984 
... Procrustes: rmse 1.482755e-06  max resid 2.629661e-06 
... Similar to previous best
Run 15 stress 0.07031998 
Run 16 stress 0.06013984 
... Procrustes: rmse 1.189837e-05  max resid 2.170195e-05 
... Similar to previous best
Run 17 stress 0.1191486 
Run 18 stress 0.1191486 
Run 19 stress 0.1201632 
Run 20 stress 0.06013984 
... Procrustes: rmse 4.230758e-06  max resid 9.107742e-06 
... Similar to previous best
Run 21 stress 0.1201645 
Run 22 stress 0.120164 
Run 23 stress 0.06013984 
... Procrustes: rmse 1.711242e-06  max resid 3.205746e-06 
... Similar to previous best
Run 24 stress 0.06013984 
... Procrustes: rmse 2.18281e-06  max resid 4.086623e-06 
... Similar to previous best
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.03577716 
Run 1 stress 0.04651979 
Run 2 stress 0.03577555 
... New best solution
... Procrustes: rmse 0.001549247  max resid 0.003208737 
... Similar to previous best
Run 3 stress 0.03577502 
... New best solution
... Procrustes: rmse 0.0007433974  max resid 0.001590529 
... Similar to previous best
Run 4 stress 0.05969502 
Run 5 stress 0.05753571 
Run 6 stress 0.0357749 
... New best solution
... Procrustes: rmse 0.0004667569  max resid 0.0009666398 
... Similar to previous best
Run 7 stress 0.04651983 
Run 8 stress 0.03577568 
... Procrustes: rmse 0.0007953663  max resid 0.001686929 
... Similar to previous best
Run 9 stress 0.05753603 
Run 10 stress 0.05753569 
Run 11 stress 0.0357751 
... Procrustes: rmse 0.0002701018  max resid 0.0005744852 
... Similar to previous best
Run 12 stress 0.05753568 
Run 13 stress 0.04264545 
Run 14 stress 0.04255826 
Run 15 stress 0.3011211 
Run 16 stress 0.03577486 
... New best solution
... Procrustes: rmse 0.0002169768  max resid 0.0004502314 
... Similar to previous best
Run 17 stress 0.04255852 
Run 18 stress 0.05753569 
Run 19 stress 0.04651982 
Run 20 stress 0.04255823 
Run 21 stress 0.0575357 
Run 22 stress 0.05753577 
Run 23 stress 0.05969527 
Run 24 stress 0.05969511 
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.002232437 
Run 1 stress 0.001997919 
... New best solution
... Procrustes: rmse 0.03398822  max resid 0.0802936 
Run 2 stress 0.002874878 
Run 3 stress 9.352181e-05 
... New best solution
... Procrustes: rmse 0.03610696  max resid 0.05347434 
Run 4 stress 9.795779e-05 
... Procrustes: rmse 0.000109017  max resid 0.0002649567 
... Similar to previous best
Run 5 stress 0.2795898 
Run 6 stress 0.002084132 
Run 7 stress 0.003914176 
Run 8 stress 0.0009390012 
Run 9 stress 0.0003463769 
... Procrustes: rmse 0.00668874  max resid 0.01001875 
Run 10 stress 9.402995e-05 
... Procrustes: rmse 0.0001740919  max resid 0.0003706251 
... Similar to previous best
Run 11 stress 0.001612211 
Run 12 stress 9.470932e-05 
... Procrustes: rmse 0.0001915933  max resid 0.0004653158 
... Similar to previous best
Run 13 stress 0.00174054 
Run 14 stress 0.002527302 
Run 15 stress 0.001775259 
Run 16 stress 0.003468988 
Run 17 stress 0.003380089 
Run 18 stress 0.0009041935 
Run 19 stress 0.004412429 
Run 20 stress 0.002200873 
Run 21 stress 0.002238389 
Run 22 stress 9.981772e-05 
... Procrustes: rmse 0.0001820388  max resid 0.0003861817 
... Similar to previous best
Run 23 stress 0.2809638 
Run 24 stress 8.73889e-05 
... New best solution
... Procrustes: rmse 0.0001913962  max resid 0.0004004257 
... Similar to previous best
*** Solution reached
stress is (nearly) zero: you may have insufficient data

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero

Run 0 stress 9.715646e-05 
Run 1 stress 0.1650199 
Run 2 stress 0.3209238 
Run 3 stress 0.001788126 
Run 4 stress 9.912951e-05 
... Procrustes: rmse 0.06628366  max resid 0.1319275 
Run 5 stress 6.098414e-05 
... New best solution
... Procrustes: rmse 0.07838964  max resid 0.144714 
Run 6 stress 0.0001252884 
... Procrustes: rmse 0.0882284  max resid 0.1608503 
Run 7 stress 9.793007e-05 
... Procrustes: rmse 0.04231372  max resid 0.08277923 
Run 8 stress 9.947279e-05 
... Procrustes: rmse 0.06478926  max resid 0.09604876 
Run 9 stress 9.60471e-05 
... Procrustes: rmse 0.1308911  max resid 0.2414282 
Run 10 stress 9.691148e-05 
... Procrustes: rmse 0.06781632  max resid 0.1158235 
Run 11 stress 9.410868e-05 
... Procrustes: rmse 0.137796  max resid 0.2929435 
Run 12 stress 0.1650199 
Run 13 stress 9.998345e-05 
... Procrustes: rmse 0.1444623  max resid 0.3319381 
Run 14 stress 9.976061e-05 
... Procrustes: rmse 0.1624868  max resid 0.3475654 
Run 15 stress 9.945545e-05 
... Procrustes: rmse 0.0685872  max resid 0.1265135 
Run 16 stress 8.382267e-05 
... Procrustes: rmse 0.1037207  max resid 0.199643 
Run 17 stress 7.592498e-05 
... Procrustes: rmse 0.09019872  max resid 0.2078087 
Run 18 stress 9.641741e-05 
... Procrustes: rmse 0.09552767  max resid 0.2317297 
Run 19 stress 0.3209239 
Run 20 stress 0.3017346 
Run 21 stress 0.0003146586 
... Procrustes: rmse 0.05260256  max resid 0.1231596 
Run 22 stress 9.958014e-05 
... Procrustes: rmse 0.09265857  max resid 0.2163928 
Run 23 stress 9.564415e-05 
... Procrustes: rmse 0.185973  max resid 0.3985334 
Run 24 stress 9.876548e-05 
... Procrustes: rmse 0.1348124  max resid 0.2522748 
Run 25 stress 0.0002608128 
... Procrustes: rmse 0.08627549  max resid 0.1597223 
Run 26 stress 9.912719e-05 
... Procrustes: rmse 0.08958133  max resid 0.2025491 
Run 27 stress 9.959469e-05 
... Procrustes: rmse 0.06775683  max resid 0.1344487 
Run 28 stress 9.954304e-05 
... Procrustes: rmse 0.09906683  max resid 0.2504429 
Run 29 stress 9.96858e-05 
... Procrustes: rmse 0.1085739  max resid 0.2129246 
Run 30 stress 9.328647e-05 
... Procrustes: rmse 0.1350035  max resid 0.2374186 
Run 31 stress 8.601448e-05 
... Procrustes: rmse 0.1225295  max resid 0.2482343 
Run 32 stress 0.1650199 
Run 33 stress 9.679553e-05 
... Procrustes: rmse 0.1079608  max resid 0.2119601 
Run 34 stress 9.884363e-05 
... Procrustes: rmse 0.07484962  max resid 0.1402191 
Run 35 stress 0.1650199 
Run 36 stress 0.1650199 
Run 37 stress 9.37702e-05 
... Procrustes: rmse 0.1283848  max resid 0.1977528 
Run 38 stress 9.323913e-05 
... Procrustes: rmse 0.08224297  max resid 0.1650759 
Run 39 stress 9.61274e-05 
... Procrustes: rmse 0.05209736  max resid 0.09928565 
Run 40 stress 9.888498e-05 
... Procrustes: rmse 0.1288244  max resid 0.2476049 
Run 41 stress 9.291898e-05 
... Procrustes: rmse 0.1333263  max resid 0.304432 
Run 42 stress 0.0004374627 
... Procrustes: rmse 0.08095838  max resid 0.1221685 
Run 43 stress 9.28992e-05 
... Procrustes: rmse 0.06892257  max resid 0.1142821 
Run 44 stress 9.780598e-05 
... Procrustes: rmse 0.09338538  max resid 0.2228937 
Run 45 stress 9.132837e-05 
... Procrustes: rmse 0.1787128  max resid 0.3730492 
Run 46 stress 9.438506e-05 
... Procrustes: rmse 0.1180555  max resid 0.302646 
Run 47 stress 8.937148e-05 
... Procrustes: rmse 0.1177609  max resid 0.2436194 
Run 48 stress 0.0001053967 
... Procrustes: rmse 0.107224  max resid 0.2327914 
Run 49 stress 9.572375e-05 
... Procrustes: rmse 0.08309477  max resid 0.159519 
Run 50 stress 9.865761e-05 
... Procrustes: rmse 0.1392818  max resid 0.291665 
Run 51 stress 9.914654e-05 
... Procrustes: rmse 0.05943651  max resid 0.09955262 
Run 52 stress 0.0002494261 
... Procrustes: rmse 0.1213787  max resid 0.2523826 
Run 53 stress 9.388338e-05 
... Procrustes: rmse 0.1730467  max resid 0.3814466 
Run 54 stress 8.589095e-05 
... Procrustes: rmse 0.1472941  max resid 0.3309337 
Run 55 stress 0.1650199 
Run 56 stress 0.1650199 
*** No convergence -- monoMDS stopping criteria:
     7: no. of iterations >= maxit
    39: stress < smin
     7: stress ratio > sratmax
     3: scale factor of the gradient < sfgrmin
stress is (nearly) zero: you may have insufficient data

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
some squared distances are negative and changed to zero

Run 0 stress 0.002262283 
Run 1 stress 8.368764e-05 
... New best solution
... Procrustes: rmse 0.04257156  max resid 0.08210284 
Run 2 stress 0.0002851804 
... Procrustes: rmse 0.005632514  max resid 0.01071809 
Run 3 stress 0.002943677 
Run 4 stress 0.01129741 
Run 5 stress 0.3209238 
Run 6 stress 0.005096398 
Run 7 stress 0.0004746828 
... Procrustes: rmse 0.009472061  max resid 0.01814479 
Run 8 stress 0.003108479 
Run 9 stress 0.0001702689 
... Procrustes: rmse 0.003293998  max resid 0.006240563 
... Similar to previous best
Run 10 stress 0.0002095137 
... Procrustes: rmse 0.004132105  max resid 0.00784589 
... Similar to previous best
Run 11 stress 0.002870141 
Run 12 stress 0.003772387 
Run 13 stress 9.99381e-05 
... Procrustes: rmse 0.0009861968  max resid 0.001680875 
... Similar to previous best
Run 14 stress 8.706071e-05 
... Procrustes: rmse 0.0002403289  max resid 0.0006092737 
... Similar to previous best
Run 15 stress 0.0004795237 
... Procrustes: rmse 0.009583548  max resid 0.01830686 
Run 16 stress 0.003214142 
Run 17 stress 0.0003821361 
... Procrustes: rmse 0.00763507  max resid 0.01457071 
Run 18 stress 0.002209892 
Run 19 stress 0.0002180807 
... Procrustes: rmse 0.004306652  max resid 0.008182944 
... Similar to previous best
Run 20 stress 9.253577e-05 
... Procrustes: rmse 0.0002210722  max resid 0.0003899679 
... Similar to previous best
Run 21 stress 0.0007126221 
Run 22 stress 0.002151844 
Run 23 stress 0.003042312 
Run 24 stress 0.002676792 
*** Solution reached
stress is (nearly) zero: you may have insufficient data

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.007256534 
Run 1 stress 0.0005258356 
... New best solution
... Procrustes: rmse 0.08498685  max resid 0.1330745 
Run 2 stress 0.0001427363 
... New best solution
... Procrustes: rmse 0.004814496  max resid 0.007564717 
... Similar to previous best
Run 3 stress 0.003904954 
Run 4 stress 0.0003731737 
... Procrustes: rmse 0.00308517  max resid 0.004842731 
... Similar to previous best
Run 5 stress 0.002248379 
Run 6 stress 0.006319521 
Run 7 stress 0.002206137 
Run 8 stress 0.007584875 
Run 9 stress 0.001601641 
Run 10 stress 0.004860352 
Run 11 stress 0.003032102 
Run 12 stress 0.0007629812 
Run 13 stress 0.001040513 
Run 14 stress 0.003940634 
Run 15 stress 0.006256498 
Run 16 stress 0.004294185 
Run 17 stress 0.00764752 
Run 18 stress 0.005972306 
Run 19 stress 0.006991458 
Run 20 stress 0.001412438 
Run 21 stress 0.003185151 
Run 22 stress 0.002065363 
Run 23 stress 0.3209238 
Run 24 stress 0.001362609 
*** Solution reached
stress is (nearly) zero: you may have insufficient data

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.001146298 
Run 1 stress 0.0006114779 
... New best solution
... Procrustes: rmse 0.07669387  max resid 0.1308177 
Run 2 stress 9.840636e-05 
... New best solution
... Procrustes: rmse 0.09173068  max resid 0.1748167 
Run 3 stress 8.008476e-05 
... New best solution
... Procrustes: rmse 0.0115157  max resid 0.02424513 
Run 4 stress 9.857295e-05 
... Procrustes: rmse 0.102126  max resid 0.2167762 
Run 5 stress 0.0005577729 
... Procrustes: rmse 0.09007764  max resid 0.2107307 
Run 6 stress 0.0006820098 
Run 7 stress 0.001055969 
Run 8 stress 0.2247492 
Run 9 stress 0.0002490661 
... Procrustes: rmse 0.0950866  max resid 0.1879391 
Run 10 stress 0.0001236696 
... Procrustes: rmse 0.07140971  max resid 0.1387873 
Run 11 stress 0.000932353 
Run 12 stress 9.923291e-05 
... Procrustes: rmse 0.1529795  max resid 0.2801125 
Run 13 stress 0.001199175 
Run 14 stress 5.7183e-05 
... New best solution
... Procrustes: rmse 0.1790086  max resid 0.2863295 
Run 15 stress 0.0001655761 
... Procrustes: rmse 0.1931575  max resid 0.3974455 
Run 16 stress 0.0006086659 
Run 17 stress 6.240911e-05 
... Procrustes: rmse 0.02762979  max resid 0.06639899 
Run 18 stress 9.772393e-05 
... Procrustes: rmse 0.05410582  max resid 0.1198345 
Run 19 stress 0.0008159551 
Run 20 stress 9.962655e-05 
... Procrustes: rmse 0.1091875  max resid 0.2106903 
Run 21 stress 0.001204008 
Run 22 stress 9.973635e-05 
... Procrustes: rmse 0.1717105  max resid 0.3667307 
Run 23 stress 9.579296e-05 
... Procrustes: rmse 0.0255935  max resid 0.05148169 
Run 24 stress 8.503145e-05 
... Procrustes: rmse 0.02377277  max resid 0.05987887 
Run 25 stress 9.900052e-05 
... Procrustes: rmse 0.02828592  max resid 0.06684035 
Run 26 stress 0.0007324996 
Run 27 stress 0.0003874549 
... Procrustes: rmse 0.1747273  max resid 0.3772563 
Run 28 stress 9.232995e-05 
... Procrustes: rmse 0.0476767  max resid 0.08280653 
Run 29 stress 0.2234307 
Run 30 stress 8.940625e-05 
... Procrustes: rmse 0.1070105  max resid 0.2460806 
Run 31 stress 9.998654e-05 
... Procrustes: rmse 0.1598778  max resid 0.3639191 
Run 32 stress 9.036142e-05 
... Procrustes: rmse 0.2030127  max resid 0.5168994 
Run 33 stress 9.99607e-05 
... Procrustes: rmse 0.1958642  max resid 0.485691 
Run 34 stress 9.56262e-05 
... Procrustes: rmse 0.05614366  max resid 0.121565 
Run 35 stress 8.504623e-05 
... Procrustes: rmse 0.06353254  max resid 0.1538668 
Run 36 stress 9.135703e-05 
... Procrustes: rmse 0.1452069  max resid 0.2561196 
Run 37 stress 0.2892217 
Run 38 stress 0.0001173512 
... Procrustes: rmse 0.1675575  max resid 0.3527847 
Run 39 stress 9.913454e-05 
... Procrustes: rmse 0.188475  max resid 0.4655414 
Run 40 stress 9.939418e-05 
... Procrustes: rmse 0.03167372  max resid 0.0733904 
Run 41 stress 0.00128497 
Run 42 stress 9.728626e-05 
... Procrustes: rmse 0.1867012  max resid 0.3669181 
Run 43 stress 9.80644e-05 
... Procrustes: rmse 0.08678116  max resid 0.1888868 
Run 44 stress 9.709794e-05 
... Procrustes: rmse 0.02477011  max resid 0.04748359 
Run 45 stress 9.995431e-05 
... Procrustes: rmse 0.07585062  max resid 0.1441218 
Run 46 stress 0.2233664 
Run 47 stress 0.0007823092 
Run 48 stress 9.870737e-05 
... Procrustes: rmse 0.1908304  max resid 0.4547034 
Run 49 stress 0.001056249 
Run 50 stress 9.615189e-05 
... Procrustes: rmse 0.02080678  max resid 0.05218457 
Run 51 stress 9.372442e-05 
... Procrustes: rmse 0.1642312  max resid 0.4050803 
Run 52 stress 9.741453e-05 
... Procrustes: rmse 0.1716041  max resid 0.3460992 
Run 53 stress 0.0007855004 
Run 54 stress 0.0002756475 
... Procrustes: rmse 0.1692496  max resid 0.3648376 
Run 55 stress 9.648228e-05 
... Procrustes: rmse 0.02304227  max resid 0.05831588 
Run 56 stress 9.697157e-05 
... Procrustes: rmse 0.02299702  max resid 0.0555012 
*** No convergence -- monoMDS stopping criteria:
    20: no. of iterations >= maxit
    32: stress < smin
     1: stress ratio > sratmax
     3: scale factor of the gradient < sfgrmin
stress is (nearly) zero: you may have insufficient data

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.09468161 
Run 1 stress 0.1092528 
Run 2 stress 0.09468161 
... New best solution
... Procrustes: rmse 4.124787e-06  max resid 8.412769e-06 
... Similar to previous best
Run 3 stress 0.09765713 
Run 4 stress 0.09468161 
... New best solution
... Procrustes: rmse 4.31468e-06  max resid 6.384544e-06 
... Similar to previous best
Run 5 stress 0.1041309 
Run 6 stress 0.09323891 
... New best solution
... Procrustes: rmse 0.1979309  max resid 0.4150472 
Run 7 stress 0.09980285 
Run 8 stress 0.1092541 
Run 9 stress 0.247493 
Run 10 stress 0.09468161 
Run 11 stress 0.1041307 
Run 12 stress 0.1041307 
Run 13 stress 0.09765718 
Run 14 stress 0.09323787 
... New best solution
... Procrustes: rmse 0.0003564355  max resid 0.0007826 
... Similar to previous best
Run 15 stress 0.09323929 
... Procrustes: rmse 0.0005209651  max resid 0.001123011 
... Similar to previous best
Run 16 stress 0.09323846 
... Procrustes: rmse 0.0002428062  max resid 0.000483184 
... Similar to previous best
Run 17 stress 0.09468168 
Run 18 stress 0.09468169 
Run 19 stress 0.09323992 
... Procrustes: rmse 0.000670326  max resid 0.001453359 
... Similar to previous best
Run 20 stress 0.09980303 
Run 21 stress 0.09809357 
Run 22 stress 0.1092533 
Run 23 stress 0.093238 
... Procrustes: rmse 3.700314e-05  max resid 7.251982e-05 
... Similar to previous best
Run 24 stress 0.09980312 
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.02255393 
Run 1 stress 0.02255394 
... Procrustes: rmse 5.890008e-05  max resid 0.0001230387 
... Similar to previous best
Run 2 stress 0.04904836 
Run 3 stress 0.02255393 
... New best solution
... Procrustes: rmse 1.259295e-05  max resid 1.957772e-05 
... Similar to previous best
Run 4 stress 0.02255391 
... New best solution
... Procrustes: rmse 1.616999e-05  max resid 3.125392e-05 
... Similar to previous best
Run 5 stress 0.02255392 
... Procrustes: rmse 3.348823e-05  max resid 7.28367e-05 
... Similar to previous best
Run 6 stress 0.02255392 
... Procrustes: rmse 3.093981e-05  max resid 6.788791e-05 
... Similar to previous best
Run 7 stress 0.0490484 
Run 8 stress 0.04904836 
Run 9 stress 0.2539725 
Run 10 stress 0.04904843 
Run 11 stress 0.04904847 
Run 12 stress 0.04904836 
Run 13 stress 0.02255391 
... New best solution
... Procrustes: rmse 1.158434e-05  max resid 2.232686e-05 
... Similar to previous best
Run 14 stress 0.02255391 
... New best solution
... Procrustes: rmse 1.121923e-05  max resid 2.34532e-05 
... Similar to previous best
Run 15 stress 0.04904836 
Run 16 stress 0.04904836 
Run 17 stress 0.02255392 
... Procrustes: rmse 7.145921e-06  max resid 1.240142e-05 
... Similar to previous best
Run 18 stress 0.04904836 
Run 19 stress 0.02255394 
... Procrustes: rmse 4.983671e-05  max resid 0.0001108371 
... Similar to previous best
Run 20 stress 0.02255391 
... Procrustes: rmse 1.049099e-05  max resid 2.280169e-05 
... Similar to previous best
Run 21 stress 0.04904836 
Run 22 stress 0.04904852 
Run 23 stress 0.02255392 
... Procrustes: rmse 2.870542e-05  max resid 6.258551e-05 
... Similar to previous best
Run 24 stress 0.04904838 
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.0756027 
Run 1 stress 0.0756027 
... Procrustes: rmse 2.153399e-05  max resid 4.290361e-05 
... Similar to previous best
Run 2 stress 0.0756027 
... Procrustes: rmse 3.397965e-05  max resid 6.30989e-05 
... Similar to previous best
Run 3 stress 0.0756027 
... Procrustes: rmse 2.624847e-05  max resid 4.50742e-05 
... Similar to previous best
Run 4 stress 0.0756027 
... New best solution
... Procrustes: rmse 7.650158e-06  max resid 1.329423e-05 
... Similar to previous best
Run 5 stress 0.07560271 
... Procrustes: rmse 6.481688e-05  max resid 0.0001202426 
... Similar to previous best
Run 6 stress 0.1386357 
Run 7 stress 0.07560279 
... Procrustes: rmse 0.0001782517  max resid 0.0003279126 
... Similar to previous best
Run 8 stress 0.170395 
Run 9 stress 0.0756027 
... Procrustes: rmse 1.158987e-05  max resid 1.959359e-05 
... Similar to previous best
Run 10 stress 0.0756027 
... Procrustes: rmse 7.08347e-06  max resid 1.324901e-05 
... Similar to previous best
Run 11 stress 0.1386357 
Run 12 stress 0.0756027 
... Procrustes: rmse 3.313062e-06  max resid 6.033171e-06 
... Similar to previous best
Run 13 stress 0.07560276 
... Procrustes: rmse 0.0001354551  max resid 0.0002480682 
... Similar to previous best
Run 14 stress 0.0756027 
... Procrustes: rmse 2.437493e-06  max resid 4.569383e-06 
... Similar to previous best
Run 15 stress 0.0756027 
... Procrustes: rmse 1.190269e-05  max resid 2.152129e-05 
... Similar to previous best
Run 16 stress 0.0756027 
... Procrustes: rmse 4.204414e-06  max resid 7.691921e-06 
... Similar to previous best
Run 17 stress 0.07560274 
... Procrustes: rmse 0.000100163  max resid 0.000181372 
... Similar to previous best
Run 18 stress 0.0756027 
... Procrustes: rmse 2.642739e-05  max resid 4.885347e-05 
... Similar to previous best
Run 19 stress 0.07560271 
... Procrustes: rmse 2.941763e-05  max resid 4.499293e-05 
... Similar to previous best
Run 20 stress 0.07560271 
... Procrustes: rmse 5.29137e-05  max resid 9.774804e-05 
... Similar to previous best
Run 21 stress 0.2420206 
Run 22 stress 0.0756027 
... Procrustes: rmse 1.484627e-05  max resid 2.723898e-05 
... Similar to previous best
Run 23 stress 0.0756027 
... Procrustes: rmse 4.7863e-05  max resid 8.870301e-05 
... Similar to previous best
Run 24 stress 0.0756027 
... Procrustes: rmse 2.011567e-05  max resid 3.764673e-05 
... Similar to previous best
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 9.998996e-05 
Run 1 stress 8.982222e-05 
... New best solution
... Procrustes: rmse 0.04498129  max resid 0.09502346 
Run 2 stress 9.458521e-05 
... Procrustes: rmse 0.008477069  max resid 0.01793451 
Run 3 stress 0.0003032633 
... Procrustes: rmse 0.05248099  max resid 0.1090391 
Run 4 stress 9.897638e-05 
... Procrustes: rmse 0.03773673  max resid 0.07989215 
Run 5 stress 0.001029345 
Run 6 stress 9.957612e-05 
... Procrustes: rmse 0.1431176  max resid 0.2768318 
Run 7 stress 0.0003786798 
... Procrustes: rmse 0.0107162  max resid 0.01984426 
Run 8 stress 9.781639e-05 
... Procrustes: rmse 0.03776795  max resid 0.0799525 
Run 9 stress 0.000173911 
... Procrustes: rmse 0.05407554  max resid 0.1148839 
Run 10 stress 0.00102935 
Run 11 stress 0.0003584027 
... Procrustes: rmse 0.01201585  max resid 0.01989333 
Run 12 stress 9.897916e-05 
... Procrustes: rmse 0.03773304  max resid 0.07994271 
Run 13 stress 0.0001419571 
... Procrustes: rmse 0.02190399  max resid 0.04374682 
Run 14 stress 0.3043873 
Run 15 stress 9.900028e-05 
... Procrustes: rmse 0.01481086  max resid 0.02626167 
Run 16 stress 0.0006722961 
Run 17 stress 9.757683e-05 
... Procrustes: rmse 0.0284852  max resid 0.05859385 
Run 18 stress 0.001818434 
Run 19 stress 9.844341e-05 
... Procrustes: rmse 0.03776806  max resid 0.07989408 
Run 20 stress 0.0001094373 
... Procrustes: rmse 0.08445352  max resid 0.1697529 
Run 21 stress 0.0003963194 
... Procrustes: rmse 0.0579705  max resid 0.1199147 
Run 22 stress 9.534595e-05 
... Procrustes: rmse 0.05654676  max resid 0.110169 
Run 23 stress 9.597358e-05 
... Procrustes: rmse 0.01995265  max resid 0.03912688 
Run 24 stress 0.001341222 
Run 25 stress 0.000211246 
... Procrustes: rmse 0.04055686  max resid 0.08151475 
Run 26 stress 9.728825e-05 
... Procrustes: rmse 0.008859884  max resid 0.01872675 
Run 27 stress 0.0001027518 
... Procrustes: rmse 0.1395294  max resid 0.2726191 
Run 28 stress 9.312224e-05 
... Procrustes: rmse 0.03772878  max resid 0.07987846 
Run 29 stress 0.0003249738 
... Procrustes: rmse 0.0566021  max resid 0.1186037 
Run 30 stress 9.606861e-05 
... Procrustes: rmse 0.0377706  max resid 0.07990546 
Run 31 stress 8.649417e-05 
... New best solution
... Procrustes: rmse 0.03507583  max resid 0.07413743 
Run 32 stress 9.596582e-05 
... Procrustes: rmse 0.002872238  max resid 0.005883666 
... Similar to previous best
*** Solution reached
stress is (nearly) zero: you may have insufficient data

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.04950647 
Run 1 stress 0.0470658 
... New best solution
... Procrustes: rmse 0.203486  max resid 0.4359799 
Run 2 stress 0.04706575 
... New best solution
... Procrustes: rmse 7.451799e-05  max resid 0.0001613821 
... Similar to previous best
Run 3 stress 0.04950549 
Run 4 stress 0.04950636 
Run 5 stress 0.04950542 
Run 6 stress 0.04950631 
Run 7 stress 0.04950634 
Run 8 stress 0.04950591 
Run 9 stress 0.0470657 
... New best solution
... Procrustes: rmse 0.0008500541  max resid 0.001460748 
... Similar to previous best
Run 10 stress 0.04706572 
... Procrustes: rmse 3.499225e-05  max resid 7.961403e-05 
... Similar to previous best
Run 11 stress 0.04950639 
Run 12 stress 0.04950595 
Run 13 stress 0.04706519 
... New best solution
... Procrustes: rmse 0.0003612806  max resid 0.0006282004 
... Similar to previous best
Run 14 stress 0.04950654 
Run 15 stress 0.04706524 
... Procrustes: rmse 0.0001747118  max resid 0.0002549931 
... Similar to previous best
Run 16 stress 0.04950565 
Run 17 stress 0.04950565 
Run 18 stress 0.04706542 
... Procrustes: rmse 0.0002283911  max resid 0.0004007541 
... Similar to previous best
Run 19 stress 0.0495053 
Run 20 stress 0.04706519 
... Procrustes: rmse 1.492559e-05  max resid 3.577865e-05 
... Similar to previous best
Run 21 stress 0.04706567 
... Procrustes: rmse 0.0004534959  max resid 0.0008160237 
... Similar to previous best
Run 22 stress 0.0495053 
Run 23 stress 0.04706525 
... Procrustes: rmse 0.0002056677  max resid 0.0003663592 
... Similar to previous best
Run 24 stress 0.04706544 
... Procrustes: rmse 0.0002334217  max resid 0.0004073871 
... Similar to previous best
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.05477351 
Run 1 stress 0.1007794 
Run 2 stress 0.1007795 
Run 3 stress 0.05477406 
... Procrustes: rmse 0.002319676  max resid 0.005791261 
... Similar to previous best
Run 4 stress 0.1226266 
Run 5 stress 0.132675 
Run 6 stress 0.1007794 
Run 7 stress 0.1007794 
Run 8 stress 0.2220934 
Run 9 stress 0.05477208 
... New best solution
... Procrustes: rmse 0.001611524  max resid 0.004029052 
... Similar to previous best
Run 10 stress 0.0547719 
... New best solution
... Procrustes: rmse 0.000171758  max resid 0.0004222003 
... Similar to previous best
Run 11 stress 0.05477239 
... Procrustes: rmse 0.001023054  max resid 0.002564228 
... Similar to previous best
Run 12 stress 0.2334965 
Run 13 stress 0.1007794 
Run 14 stress 0.1211966 
Run 15 stress 0.1330476 
Run 16 stress 0.1882172 
Run 17 stress 0.1007794 
Run 18 stress 0.1316644 
Run 19 stress 0.1007794 
Run 20 stress 0.05477446 
... Procrustes: rmse 0.001667766  max resid 0.004185369 
... Similar to previous best
Run 21 stress 0.05477198 
... Procrustes: rmse 0.0007658458  max resid 0.001920631 
... Similar to previous best
Run 22 stress 0.05477203 
... Procrustes: rmse 0.0007548629  max resid 0.001901816 
... Similar to previous best
Run 23 stress 0.1596603 
Run 24 stress 0.05477221 
... Procrustes: rmse 0.0002422576  max resid 0.0005924589 
... Similar to previous best
*** Solution reached

`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.

Run 0 stress 0.0004631135 
Run 1 stress 0.03350798 
Run 2 stress 0.0004435229 
... New best solution
... Procrustes: rmse 0.1006707  max resid 0.149066 
Run 3 stress 0.0003188924 
... New best solution
... Procrustes: rmse 0.1122904  max resid 0.1682682 
Run 4 stress 0.01733724 
Run 5 stress 0.01733875 
Run 6 stress 0.030171 
Run 7 stress 0.01733898 
Run 8 stress 0.0008839934 
Run 9 stress 0.01733722 
Run 10 stress 0.0002129749 
... New best solution
... Procrustes: rmse 0.000685512  max resid 0.001162808 
... Similar to previous best
Run 11 stress 0.01733742 
Run 12 stress 0.01733715 
Run 13 stress 0.03016628 
Run 14 stress 0.0002134977 
... Procrustes: rmse 0.003469167  max resid 0.006034373 
... Similar to previous best
Run 15 stress 0.0004995572 
... Procrustes: rmse 0.03805641  max resid 0.06778901 
Run 16 stress 0.01733795 
Run 17 stress 0.01733842 
Run 18 stress 0.000104317 
... New best solution
... Procrustes: rmse 0.004241015  max resid 0.007917967 
... Similar to previous best
Run 19 stress 0.0002726536 
... Procrustes: rmse 0.004443645  max resid 0.008287683 
... Similar to previous best
Run 20 stress 0.0007836847 
Run 21 stress 0.0006999267 
Run 22 stress 0.1820573 
Run 23 stress 0.0003537421 
... Procrustes: rmse 0.004832757  max resid 0.008882361 
... Similar to previous best
Run 24 stress 0.000149104 
... Procrustes: rmse 0.003959221  max resid 0.007466465 
... Similar to previous best
*** Solution reached
stress is (nearly) zero: you may have insufficient data

[[1]]
[[1]][[1]]
$sites
               PCoA1         PCoA2
FRP_3_3 -0.005547325  0.0005045195
FRP_2_3 -0.004488276  0.0065360219
FRP_1_1  0.011477634 -0.0003269092
FRP_1_2 -0.011611522 -0.0066670249
FRP_1_3 -0.000423844 -0.0012355197
FRP_2_1  0.005187778 -0.0014141226
FRP_2_2  0.008881584 -0.0053496524
FRP_3_1 -0.008074636  0.0018758707
FRP_3_2  0.004598607  0.0060768166

$centroids
          PCoA1        PCoA2
1 -0.0004238438 -0.001235520
2  0.0051877747 -0.001414123
3 -0.0052787933  0.001403469

attr(,"class")
[1] "ordiplot"

[[1]][[2]]
[[1]][[2]]$x
 [1] 0.005396763 0.017771699 0.010382880 0.005816994 0.011580077 0.015433240 0.004486533
 [8] 0.013251058 0.017887791 0.015442821 0.008185814 0.013040246 0.018200377 0.007591078
[15] 0.010756449 0.024517523 0.012460151 0.008353108 0.008734111 0.019896871 0.012481253
[22] 0.012562818 0.017985810 0.020873404 0.010194995 0.020826922 0.006533164 0.010448968
[29] 0.009237201 0.010398506 0.006946441 0.014091633 0.009257227 0.019251765 0.012793839
[36] 0.014810298

[[1]][[2]]$y
 [1] 0.011275665 0.032428596 0.015822750 0.010198500 0.020162382 0.027125607 0.006426471
 [8] 0.023111502 0.033463385 0.025267137 0.013855811 0.023081579 0.032212613 0.010040610
[15] 0.017633458 0.045389253 0.022290796 0.012430187 0.012140820 0.038008904 0.019958792
[22] 0.024728215 0.033060925 0.036986642 0.015438978 0.038901913 0.010254165 0.018553422
[29] 0.015783976 0.015500607 0.009729944 0.026015056 0.015792867 0.033452624 0.024920270
[36] 0.026015080

[[1]][[2]]$yf
 [1] 0.010299777 0.032428596 0.015667836 0.010299777 0.020162382 0.026196372 0.006426471
 [8] 0.023960392 0.032912308 0.026196372 0.012808940 0.023960392 0.032912308 0.010299777
[15] 0.018093440 0.045389253 0.021124794 0.012808940 0.012808940 0.037965820 0.021124794
[22] 0.023960392 0.032912308 0.037965820 0.015667836 0.037965820 0.010299777 0.018093440
[29] 0.015667836 0.015667836 0.010299777 0.026015056 0.015667836 0.033452624 0.023960392
[36] 0.026015080


[[1]][[3]]


[[2]]
[[2]][[1]]
$sites
              PCoA1       PCoA2
FRP_1_1  0.20487958  0.10165109
FRP_1_2 -0.06646233 -0.04206991
FRP_3_3  0.29072760  0.13272390
FRP_1_3  0.16153549 -0.16057535
FRP_2_1 -0.01826230 -0.03584191
FRP_2_2 -0.05973786 -0.19051412
FRP_2_3 -0.06037862  0.03552878
FRP_3_1 -0.31203833  0.13881484
FRP_3_2 -0.14026323  0.02028267

$centroids
        PCoA1       PCoA2
1  0.10366541 -0.03119683
2 -0.04701556 -0.07115274
3 -0.10376027  0.07511796

attr(,"class")
[1] "ordiplot"

[[2]][[2]]
[[2]][[2]]$x
 [1] 0.3810377 0.2499316 0.3743336 0.3622436 0.4359445 0.3898673 0.5719967 0.4197235 0.4659818
[10] 0.3906026 0.2534814 0.3377097 0.3715432 0.4171329 0.3005332 0.3779329 0.4136108 0.5036109
[19] 0.4488573 0.6220341 0.5149630 0.3549234 0.3675838 0.4202281 0.5742692 0.4649826 0.3320846
[28] 0.3597726 0.4324230 0.3027710 0.3301834 0.4697905 0.3681059 0.4079380 0.3527422 0.3963990

[[2]][[2]]$y
 [1] 0.40798746 0.11052997 0.30262601 0.33229061 0.48655804 0.52912930 0.87323323 0.45237770
 [9] 0.51612284 0.48180816 0.08347515 0.32803515 0.29834901 0.47113548 0.05031547 0.30844110
[17] 0.43830791 0.55688350 0.60917209 0.97747343 0.56147412 0.40238248 0.33137159 0.40631073
[25] 0.85665036 0.53156880 0.29479708 0.28598772 0.54116654 0.13353068 0.08078603 0.54869919
[33] 0.36102024 0.46803864 0.32205739 0.43569923

[[2]][[2]]$yf
 [1] 0.4079875 0.0814402 0.3278086 0.3278086 0.5138623 0.4603509 0.8649418 0.4603509 0.5513907
[10] 0.4603509 0.0814402 0.3250463 0.3278086 0.4603509 0.0814402 0.3278086 0.4603509 0.5568835
[19] 0.5513907 0.9774734 0.5614741 0.3278086 0.3278086 0.4603509 0.8649418 0.5513907 0.2947971
[28] 0.3278086 0.5138623 0.1071584 0.1071584 0.5513907 0.3278086 0.4603509 0.3250463 0.4603509


[[2]][[3]]


[[3]]
[[3]][[1]]
$sites
               PCoA1        PCoA2
FRP_3_1 -0.001931237  0.012246537
FRP_1_1 -0.006034021 -0.032972981
FRP_1_3  0.011451056 -0.015143387
FRP_3_2 -0.060762716  0.029059786
FRP_3_3  0.062309100  0.034468914
FRP_1_2  0.013151240 -0.030047175
FRP_2_1 -0.006817757 -0.001986124
FRP_2_2 -0.004472375  0.011522298
FRP_2_3 -0.006893289 -0.007147867

$centroids
         PCoA1        PCoA2
1  0.007183662 -0.025043923
2 -0.006380038 -0.001474969
3 -0.001842308  0.015818376

attr(,"class")
[1] "ordiplot"

[[3]][[2]]
[[3]][[2]]$x
 [1] 0.06318859 0.04936829 0.07329592 0.07843213 0.06209693 0.05641769 0.05425318 0.04935059
 [9] 0.04825470 0.08760758 0.09824942 0.05146963 0.05700800 0.06499605 0.05674382 0.08925677
[17] 0.07714945 0.04326066 0.05134237 0.05455261 0.04908788 0.12390016 0.09773073 0.07868693
[25] 0.07453743 0.08054167 0.08711542 0.08708963 0.08206173 0.08976073 0.06644117 0.06332477
[33] 0.06027328 0.05295257 0.03421111 0.05105556

[[3]][[2]]$y
 [1] 1.201968e-05 4.201994e-06 6.665848e-02 6.666525e-02 1.350483e-05 3.674534e-06
 [7] 4.357333e-06 6.277624e-06 7.840480e-06 6.665850e-02 6.666835e-02 1.666813e-06
[13] 8.695638e-06 9.660717e-06 6.664432e-06 6.665884e-02 6.666599e-02 9.365316e-06
[19] 1.845371e-06 3.807539e-06 3.189595e-06 1.321780e-01 6.665770e-02 6.665715e-02
[25] 6.665524e-02 6.665609e-02 6.666950e-02 6.666742e-02 6.666913e-02 6.666906e-02
[31] 1.006871e-05 1.084072e-05 7.843553e-06 1.975078e-06 2.605253e-06 3.003779e-06

[[3]][[2]]$yf
 [1] 1.121893e-05 4.267121e-06 6.665686e-02 6.666112e-02 1.121893e-05 4.267121e-06
 [7] 4.267121e-06 4.267121e-06 4.267121e-06 6.666431e-02 6.666835e-02 4.267121e-06
[13] 8.269595e-06 1.121893e-05 6.664432e-06 6.666431e-02 6.666112e-02 4.267121e-06
[19] 4.267121e-06 4.267121e-06 4.267121e-06 1.321780e-01 6.666431e-02 6.666112e-02
[25] 6.665686e-02 6.666112e-02 6.666431e-02 6.666431e-02 6.666431e-02 6.666431e-02
[31] 1.121893e-05 1.121893e-05 8.269595e-06 4.267121e-06 2.605253e-06 4.267121e-06


[[3]][[3]]


[[4]]
[[4]][[1]]
$sites
              PCoA1       PCoA2
FRP_2_2  0.17799255  0.25183997
FRP_3_1 -0.05901937 -0.00314384
FRP_2_3  0.10527545 -0.15435191
FRP_1_2 -0.38534194  0.08012980
FRP_3_2  0.27659212  0.07722254
FRP_1_1  0.10502289 -0.15861495
FRP_1_3  0.05989809  0.02930808
FRP_2_1  0.17327710 -0.09548819
FRP_3_3 -0.45369688 -0.02690150

$centroids
        PCoA1       PCoA2
1  0.02194434 -0.01639378
2  0.15360835 -0.06030009
3 -0.05901942 -0.00314379

attr(,"class")
[1] "ordiplot"

[[4]][[2]]
[[4]][[2]]$x
 [1] 0.35384841 0.42506718 0.60801190 0.25985575 0.43398207 0.26677392 0.34322605 0.68484990
 [9] 0.25619048 0.43431500 0.42063846 0.26697464 0.23898258 0.35873218 0.39788449 0.55392810
[17] 0.28242819 0.07431101 0.20582591 0.17225208 0.56528116 0.66992075 0.55770060 0.48717296
[25] 0.60659752 0.17824954 0.28050839 0.31416901 0.36873608 0.75429305 0.21075361 0.20141782
[33] 0.56696407 0.29137914 0.52747716 0.65931473

[[4]][[2]]$y
 [1] 0.64493762 0.62385052 1.20420242 0.27276544 0.62765660 0.42143840 0.73622366 1.32379626
 [9] 0.35359124 0.64762321 0.71805405 0.37981572 0.24454404 0.61450308 0.70523771 0.97417919
[17] 0.55027960 0.02634833 0.27780129 0.26102297 0.97830101 1.34425400 0.99992967 0.87725199
[25] 1.22227750 0.24059616 0.54236450 0.47408912 0.56299459 1.42328093 0.29565683 0.23495718
[33] 1.00194072 0.50417357 0.94941327 1.20251638

[[4]][[2]]$yf
 [1] 0.63966474 0.66448442 1.20966543 0.31317834 0.66448442 0.40062706 0.63966474 1.33402513
 [9] 0.31317834 0.66448442 0.66448442 0.40062706 0.27266739 0.63966474 0.66448442 0.97417919
[17] 0.51772670 0.02634833 0.27266739 0.24552544 0.98911534 1.33402513 0.98911534 0.87725199
[25] 1.20966543 0.24552544 0.51772670 0.51772670 0.63966474 1.42328093 0.27266739 0.24552544
[33] 1.00194072 0.51772670 0.94941327 1.20966543


[[4]][[3]]


[[5]]
[[5]][[1]]
$sites
              PCoA1       PCoA2
FRP_1_1 -0.09342640 -0.13909724
FRP_2_1 -0.04911833 -0.05240495
FRP_2_2 -0.10688658 -0.03189644
FRP_2_3 -0.12014178 -0.04200880
FRP_3_1  0.17385655  0.03140832
FRP_3_2  0.27091070 -0.11851037
FRP_1_2  0.08524345  0.17844170
FRP_3_3  0.01561603  0.09419203
FRP_1_3 -0.17605364  0.07987576

$centroids
       PCoA1       PCoA2
1 -0.1003262  0.04289651
2 -0.1041637 -0.03904992
3  0.1539449  0.01826174

attr(,"class")
[1] "ordiplot"

[[5]][[2]]
[[5]][[2]]$x
 [1] 0.19265351 0.10539408 0.08232738 0.36725262 0.33691756 0.36306676 0.24918064 0.18388904
 [9] 0.12686567 0.14959862 0.20880964 0.36200717 0.33287338 0.20899725 0.16125654 0.05258113
[17] 0.32588054 0.38343230 0.28503586 0.17164179 0.10834571 0.31890310 0.40501792 0.30906708
[25] 0.19518097 0.12224410 0.24115522 0.26105703 0.19589059 0.36125654 0.32807336 0.33581472
[33] 0.49118955 0.12687313 0.24675325 0.17444336

[[5]][[2]]$y
 [1] 0.19857798 0.15350534 0.12787891 0.62351388 0.73055274 0.68136236 0.44232330 0.28212317
 [9] 0.22015337 0.23461059 0.43051379 0.53875978 0.56486107 0.33965397 0.32252581 0.04068146
[17] 0.57556177 0.74404001 0.55251427 0.31723963 0.13050567 0.60965593 0.76742218 0.59284974
[25] 0.35789996 0.15590779 0.30159862 0.40391090 0.36999761 0.59501924 0.70542702 0.63649648
[33] 0.80642784 0.23903972 0.47976165 0.26732717

[[5]][[2]]$yf
 [1] 0.27755875 0.14200550 0.12787891 0.65243812 0.62851939 0.65243812 0.44199862 0.27755875
 [9] 0.22015337 0.23682516 0.36044100 0.62851939 0.62851939 0.36044100 0.27755875 0.04068146
[17] 0.59268915 0.74404001 0.55251427 0.27755875 0.14200550 0.59268915 0.76742218 0.59268915
[25] 0.35789996 0.15590779 0.36044100 0.44199862 0.36044100 0.62851939 0.62851939 0.62851939
[33] 0.80642784 0.23682516 0.44199862 0.27755875


[[5]][[3]]


[[6]]
[[6]][[1]]
$sites
               PCoA1         PCoA2
FRP_3_3 -0.008690301  0.0006200565
FRP_2_2  0.040867132  0.0245199753
FRP_1_1 -0.097763848 -0.0055234469
FRP_2_1  0.023495705 -0.0392895240
FRP_1_3  0.001394753  0.0165320012
FRP_2_3  0.063308584 -0.0106077254
FRP_3_1  0.018009346  0.0400384635
FRP_1_2 -0.006321205 -0.0567512118
FRP_3_2 -0.034300167  0.0304614115

$centroids
         PCoA1        PCoA2
1 -0.025141025 -0.014909329
2  0.046035454 -0.007812144
3 -0.009501581  0.023144802

attr(,"class")
[1] "ordiplot"

[[6]][[2]]
[[6]][[2]]$x
 [1] 0.08130629 0.10487043 0.06984067 0.08598386 0.09413049 0.08083959 0.10302691 0.07534999
 [9] 0.14613330 0.08900176 0.08186569 0.07110255 0.06507338 0.11433579 0.10137255 0.13479296
[17] 0.12121608 0.16455444 0.13419102 0.12655657 0.09723785 0.09691504 0.07625878 0.10397739
[25] 0.09828325 0.10849281 0.09976004 0.06647056 0.09533031 0.08713403 0.08745315 0.10931146
[33] 0.11641142 0.10752060 0.08006624 0.11147665

[[6]][[2]]$y
 [1] 0.05524039 0.13509236 0.05907865 0.03298430 0.07572841 0.05081351 0.08125394 0.06862387
 [9] 0.17797504 0.07596921 0.05163177 0.04992511 0.03242983 0.13396709 0.08931749 0.18559583
[17] 0.12642262 0.21065367 0.15138150 0.12960012 0.09625803 0.08906042 0.05075685 0.09454591
[25] 0.08654853 0.12757480 0.09117816 0.02814488 0.10755936 0.04136296 0.08075760 0.13336554
[33] 0.13233628 0.13128883 0.05853100 0.12362521

[[6]][[2]]$yf
 [1] 0.05189484 0.13018935 0.05189484 0.05189484 0.07748507 0.05189484 0.09159656 0.05189484
 [9] 0.18178543 0.07748507 0.05189484 0.05189484 0.03028735 0.13058153 0.09159656 0.18178543
[17] 0.13058153 0.21065367 0.15138150 0.13058153 0.09159656 0.09159656 0.05189484 0.09454591
[25] 0.09159656 0.13018935 0.09159656 0.03028735 0.09159656 0.05189484 0.07748507 0.13018935
[33] 0.13058153 0.13018935 0.05189484 0.13018935


[[6]][[3]]


[[7]]
[[7]][[1]]
$sites
               PCoA1         PCoA2
FRP_1_1  0.023601242  0.0515732901
FRP_1_2 -0.114850272  0.0270821925
FRP_1_3 -0.020189353  0.0366346205
FRP_2_1 -0.086838836 -0.0527015210
FRP_2_2 -0.006495302 -0.0154075848
FRP_2_3  0.037056889 -0.0245357974
FRP_3_1  0.033152004 -0.0127861487
FRP_3_2  0.061791096 -0.0006184987
FRP_3_3  0.072772532 -0.0092405525

$centroids
         PCoA1       PCoA2
1 -0.025504218  0.03772454
2 -0.007299934 -0.01889747
3  0.059429285 -0.00738656

attr(,"class")
[1] "ordiplot"

[[7]][[2]]
[[7]][[2]]$x
 [1] 0.15634170 0.09157809 0.15505331 0.09291704 0.10652995 0.10016082 0.07872819 0.10331525
 [9] 0.10857769 0.10505967 0.12734353 0.16579471 0.16242183 0.18530877 0.19368910 0.12515976
[17] 0.08226796 0.10604328 0.09059640 0.10309450 0.11279719 0.10777242 0.14758804 0.14002590
[25] 0.16050291 0.17372128 0.07034092 0.08517276 0.08784587 0.09819521 0.08588402 0.06841429
[33] 0.07430723 0.07033473 0.06263747 0.05360504

[[7]][[2]]$y
 [1] 0.23697467 0.09362799 0.23848784 0.09805599 0.11701887 0.08365429 0.05957546 0.10988644
 [9] 0.15019554 0.11248847 0.19200864 0.25019816 0.23570915 0.26103648 0.29074122 0.14720848
[17] 0.05656594 0.11515008 0.09123422 0.11103311 0.14550977 0.15434416 0.19659818 0.19769299
[25] 0.23557736 0.24623924 0.06021986 0.04527316 0.08126264 0.09928417 0.03391762 0.06778630
[33] 0.05103516 0.04220719 0.05511256 0.05032557

[[7]][[2]]$yf
 [1] 0.23668725 0.09362799 0.23668725 0.09366482 0.11701887 0.09366482 0.05241036 0.11045978
 [9] 0.14931449 0.11248847 0.19200864 0.24821870 0.23668725 0.26103648 0.29074122 0.14931449
[17] 0.05241036 0.11515008 0.09123422 0.11045978 0.14931449 0.14931449 0.19714559 0.19714559
[25] 0.23668725 0.24821870 0.05241036 0.05241036 0.08126264 0.09366482 0.05241036 0.05241036
[33] 0.05241036 0.05241036 0.05241036 0.05032557


[[7]][[3]]


[[8]]
[[8]][[1]]
$sites
              PCoA1        PCoA2
FRP_1_1 -0.05450767 -0.026592133
FRP_1_2 -0.05358391 -0.023657084
FRP_1_3 -0.02441056 -0.043554241
FRP_2_1 -0.02794855  0.044399105
FRP_2_2  0.05302367  0.006566878
FRP_2_3 -0.05178144  0.038542878
FRP_3_1  0.08979966  0.007271322
FRP_3_2  0.10177646 -0.012233954
FRP_3_3 -0.03236765  0.009257229

$centroids
        PCoA1         PCoA2
1 -0.04602763 -3.020217e-02
2 -0.02044166  3.670164e-02
3  0.07545839 -2.907604e-05

attr(,"class")
[1] "ordiplot"

[[8]][[2]]
[[8]][[2]]$x
 [1] 0.07515191 0.08546868 0.09879401 0.13508960 0.08987725 0.15978904 0.16653838 0.07942185
 [9] 0.09054984 0.09794397 0.13124533 0.09340710 0.16082867 0.16785918 0.08399250 0.10055186
[17] 0.12399559 0.10590683 0.13888111 0.14792764 0.09686048 0.11311481 0.08211352 0.13899424
[25] 0.15580523 0.08689472 0.14121133 0.10060310 0.10003211 0.11898622 0.15580672 0.16809118
[33] 0.08079828 0.07583264 0.14253949 0.14724918

[[8]][[2]]$y
 [1] 6.206073e-10 6.228396e-05 4.311712e-05 1.209310e-01 3.909449e-05 1.209715e-01
 [7] 1.209909e-01 4.798322e-05 6.228359e-05 4.311654e-05 1.209310e-01 3.909463e-05
[13] 1.209715e-01 1.209909e-01 4.798265e-05 6.384071e-05 1.208899e-01 4.805991e-05
[19] 1.209304e-01 1.209497e-01 3.174363e-05 1.208914e-01 7.209007e-05 1.209319e-01
[25] 1.209513e-01 3.290414e-05 1.209372e-01 5.939098e-05 5.994205e-05 1.208855e-01
[31] 1.209778e-01 1.209971e-01 5.669396e-05 5.045420e-05 1.209261e-01 1.209454e-01

[[8]][[2]]$yf
 [1] 6.206073e-10 4.837248e-05 4.837248e-05 1.209308e-01 4.837248e-05 1.209736e-01
 [7] 1.209909e-01 4.837248e-05 4.837248e-05 4.837248e-05 1.209308e-01 4.837248e-05
[13] 1.209736e-01 1.209909e-01 4.837248e-05 5.780841e-05 1.208899e-01 5.780841e-05
[19] 1.209308e-01 1.209497e-01 4.837248e-05 1.208885e-01 4.837248e-05 1.209317e-01
[25] 1.209513e-01 4.837248e-05 1.209317e-01 5.780841e-05 5.780841e-05 1.208885e-01
[31] 1.209736e-01 1.209971e-01 4.837248e-05 4.837248e-05 1.209317e-01 1.209454e-01


[[8]][[3]]


[[9]]
[[9]][[1]]
$sites
                PCoA1         PCoA2
FRP_2_3 -0.0075069428 -0.0015131609
FRP_3_3 -0.0136314830 -0.0001417953
FRP_2_2  0.0119852752  0.0011382717
FRP_3_1 -0.0149908648  0.0001230846
FRP_3_2  0.0142810121 -0.0061714948
FRP_1_1  0.0268281024  0.0073935921
FRP_2_1  0.0114924888 -0.0037995548
FRP_1_2 -0.0282838824  0.0032418107
FRP_1_3 -0.0001737056 -0.0002707533

$centroids
          PCoA1         PCoA2
1 -0.0001737063 -0.0002707522
2  0.0100900982 -0.0013861505
3 -0.0128725480 -0.0003169718

attr(,"class")
[1] "ordiplot"

[[9]][[2]]
[[9]][[2]]$x
 [1] 0.009964679 0.020893640 0.011005770 0.024414744 0.036045166 0.021195981 0.024182982
 [8] 0.009234508 0.026149051 0.004845268 0.029559353 0.041402984 0.026512302 0.018188682
[15] 0.014446533 0.027029103 0.007041061 0.015520610 0.004760023 0.040311953 0.012498818
[22] 0.030097880 0.042444075 0.027417697 0.015088302 0.015129857 0.018934929 0.007832418
[29] 0.043833057 0.015674168 0.019640032 0.055621287 0.027808151 0.040730605 0.012781774
[36] 0.028652821

[[9]][[2]]$y
 [1] 2.033607e-02 3.668644e-02 2.034968e-02 4.537165e-02 7.180295e-02 3.669824e-02
 [7] 3.946734e-02 1.956070e-02 4.603613e-02 6.183093e-03 5.962436e-02 7.876267e-02
[13] 4.604305e-02 3.511790e-02 2.533712e-02 5.038645e-02 1.781359e-02 3.513925e-02
[19] 1.468126e-05 7.563339e-02 2.071643e-02 6.289250e-02 8.385799e-02 5.039453e-02
[25] 2.896378e-02 2.973505e-02 3.512724e-02 1.781835e-02 8.460337e-02 3.511599e-02
[31] 3.512703e-02 1.104193e-01 5.446639e-02 7.564405e-02 2.072374e-02 5.610601e-02

[[9]][[2]]$yf
 [1] 2.033607e-02 3.668644e-02 2.034968e-02 4.537165e-02 7.180295e-02 3.669824e-02
 [7] 3.946734e-02 1.956070e-02 4.603613e-02 6.183093e-03 5.962436e-02 7.876267e-02
[13] 4.604305e-02 3.512438e-02 2.533712e-02 5.038645e-02 1.781359e-02 3.512438e-02
[19] 1.468126e-05 7.563339e-02 2.071643e-02 6.289250e-02 8.385799e-02 5.039453e-02
[25] 2.896378e-02 2.973505e-02 3.512713e-02 1.781835e-02 8.460337e-02 3.512438e-02
[31] 3.512713e-02 1.104193e-01 5.446639e-02 7.564405e-02 2.072374e-02 5.610601e-02


[[9]][[3]]


[[10]]
[[10]][[1]]
$sites
             PCoA1         PCoA2
FRP_3_1 -0.1085354 -0.0901112650
FRP_1_1 -0.1098709  0.0682064877
FRP_2_1 -0.1120389 -0.0466669090
FRP_1_2  0.8863304 -0.0004257656
FRP_1_3 -0.1060772  0.1148038400
FRP_2_2 -0.1128938  0.0260613592
FRP_2_3 -0.1128570 -0.0250147296
FRP_3_2 -0.1109676 -0.0654725784
FRP_3_3 -0.1130896  0.0186195607

$centroids
        PCoA1       PCoA2
1 -0.08315321  0.08910282
2 -0.11285699 -0.02501473
3 -0.11079026 -0.06328318

attr(,"class")
[1] "ordiplot"

[[10]][[2]]
[[10]][[2]]$x
 [1] 0.16549405 0.05199697 0.99909451 0.20778065 0.11850635 0.07043924 0.04520604 0.10783680
 [9] 0.12278830 0.99933962 0.08638218 0.05565859 0.10475374 0.14186296 0.05779551 0.99951538
[17] 0.16489914 0.07562484 0.02755773 0.02819011 0.06499279 0.99959390 0.99967155 0.99960898
[25] 0.99959362 0.99947788 0.09769091 0.14266532 0.18389556 0.09994385 0.05749668 0.09462126
[33] 0.01066955 0.04655415 0.04695823 0.08406745

[[10]][[2]]$y
 [1] 3.013607e-01 1.284220e-01 5.551949e+02 3.836298e-01 1.945515e-01 1.157876e-01
 [7] 1.529731e-01 1.929428e-01 2.183732e-01 5.554368e+02 1.799459e-01 1.255172e-01
[13] 1.961202e-01 2.402494e-01 1.233944e-01 5.553231e+02 3.534321e-01 1.608494e-01
[19] 4.432013e-02 3.933779e-02 1.560427e-01 5.553641e+02 5.553139e+02 5.553056e+02
[25] 5.553422e+02 5.553172e+02 1.969421e-01 3.158175e-01 3.860651e-01 2.006630e-01
[31] 1.201836e-01 1.970179e-01 5.686904e-03 8.355437e-02 1.158210e-01 1.919579e-01

[[10]][[2]]$yf
 [1] 3.273964e-01 1.243793e-01 5.551949e+02 3.848474e-01 1.963729e-01 1.359151e-01
 [7] 1.174495e-01 1.963729e-01 2.183732e-01 5.553433e+02 1.859519e-01 1.243793e-01
[13] 1.963729e-01 2.402494e-01 1.243793e-01 5.553433e+02 3.273964e-01 1.608494e-01
[19] 4.182896e-02 4.182896e-02 1.359151e-01 5.553433e+02 5.553433e+02 5.553433e+02
[25] 5.553433e+02 5.553433e+02 1.963729e-01 3.158175e-01 3.848474e-01 1.963729e-01
[31] 1.243793e-01 1.963729e-01 5.686904e-03 1.174495e-01 1.174495e-01 1.859519e-01


[[10]][[3]]


[[11]]
[[11]][[1]]
$sites
              PCoA1        PCoA2
FRP_1_1 -0.09514701  0.008394403
FRP_1_2 -0.06071760 -0.031882771
FRP_1_3 -0.04370628  0.012410529
FRP_2_1 -0.01669106  0.027673785
FRP_2_2 -0.03552014 -0.037692437
FRP_2_3  0.02258852  0.031983264
FRP_3_1  0.24070467 -0.024534278
FRP_3_2  0.04257802  0.046029929
FRP_3_3 -0.05408912 -0.032382423

$centroids
         PCoA1       PCoA2
1 -0.064131389 -0.01106462
2 -0.006827185  0.01209698
3  0.044582793  0.03731557

attr(,"class")
[1] "ordiplot"

[[11]][[2]]
[[11]][[2]]$x
 [1] 0.08660329 0.10939189 0.12309256 0.11852043 0.15226257 0.34069878 0.15824574 0.10795226
 [9] 0.07844849 0.09340053 0.08260795 0.12660948 0.30333778 0.14198590 0.07380130 0.09256311
[17] 0.11608861 0.11090974 0.29284803 0.13055158 0.09706762 0.10728237 0.08820571 0.26874802
[25] 0.10063465 0.09836585 0.10528789 0.28643948 0.13186197 0.09660365 0.23498865 0.08390076
[33] 0.12702711 0.21693542 0.30100817 0.13891775

[[11]][[2]]$y
 [1] 0.0006312815 0.0010005901 0.0012613205 0.0012147068 0.0017887050 0.3856976392
 [7] 0.0019976862 0.0006488094 0.0006577296 0.0006525338 0.0006717949 0.0011589744
[13] 0.3850792774 0.0013967194 0.0000175488 0.0006221769 0.0011551701 0.0012627994
[19] 0.3848181357 0.0011729609 0.0006552198 0.0007015049 0.0006443131 0.3844364077
[25] 0.0007441860 0.0006365837 0.0007474602 0.3846856166 0.0012556796 0.0006583741
[31] 0.3839688256 0.0006284788 0.0011414382 0.3837030946 0.3850622844 0.0013807415

[[11]][[2]]$yf
 [1] 0.0006426292 0.0010005901 0.0011953386 0.0011953386 0.0017887050 0.3856976392
 [7] 0.0019976862 0.0007104901 0.0006426292 0.0006506778 0.0006426292 0.0011953386
[13] 0.3850792774 0.0013967194 0.0000175488 0.0006426292 0.0011953386 0.0011953386
[19] 0.3848181357 0.0011953386 0.0006506778 0.0007104901 0.0006426292 0.3844364077
[25] 0.0007104901 0.0006506778 0.0007104901 0.3846856166 0.0012556796 0.0006506778
[31] 0.3839688256 0.0006426292 0.0011953386 0.3837030946 0.3850622844 0.0013807415


[[11]][[3]]


[[12]]
[[12]][[1]]
$sites
              PCoA1       PCoA2
FRP_1_1  0.13421799 -0.02567026
FRP_1_2 -0.07617712 -0.07819529
FRP_1_3 -0.21674975  0.01203261
FRP_2_1 -0.17387372 -0.05905003
FRP_2_2 -0.21539203  0.08890216
FRP_2_3  0.12169325 -0.01426770
FRP_3_1  0.15342130  0.01136414
FRP_3_2  0.13276019  0.01623499
FRP_3_3  0.14009989  0.04864939

$centroids
        PCoA1       PCoA2
1 -0.07153641 -0.06026268
2 -0.14846600 -0.01367542
3  0.14185481  0.02265258

attr(,"class")
[1] "ordiplot"

[[12]][[2]]
[[12]][[2]]$x
 [1] 0.24588151 0.36140907 0.32526670 0.37797669 0.11084420 0.11211961 0.09870483 0.13075995
 [9] 0.20822483 0.15529962 0.22813233 0.23551744 0.27089162 0.24686544 0.25988053 0.15726096
[17] 0.14028697 0.35281988 0.38404300 0.36727377 0.37974387 0.16463695 0.31203666 0.34546287
[25] 0.32766757 0.34706053 0.36144526 0.38551409 0.35939730 0.36720634 0.10868044 0.09415474
[33] 0.12679968 0.10047183 0.12865198 0.12279973

[[12]][[2]]$y
 [1] 0.4384089853 0.4791168772 0.4514514986 0.4791395306 0.0001465783 0.0002739245
 [7] 0.0001274710 0.0004354685 0.4312987533 0.0683657710 0.4319238224 0.4384787960
[13] 0.4386826268 0.4385092531 0.4387687491 0.3735147496 0.0007288707 0.4790493685
[19] 0.4792807862 0.4791079983 0.4791157429 0.3741713158 0.4515011288 0.4517239003
[25] 0.4515387873 0.4517702762 0.4790718242 0.4793031058 0.4791304584 0.4791377342
[31] 0.0002480599 0.0000587041 0.0003124608 0.0001958896 0.0002718915 0.0003083990

[[12]][[2]]$yf
 [1] 0.4384438906 0.4791063866 0.4514763137 0.4791276367 0.0001973191 0.0002739245
 [7] 0.0001274710 0.0004354685 0.4312987533 0.0683657710 0.4319238224 0.4384438906
[13] 0.4387256880 0.4385092531 0.4387256880 0.3735147496 0.0007288707 0.4790493685
[19] 0.4792807862 0.4791228662 0.4791276367 0.3741713158 0.4514763137 0.4517239003
[25] 0.4515387873 0.4517702762 0.4791063866 0.4793031058 0.4791063866 0.4791228662
[31] 0.0001973191 0.0000587041 0.0002975838 0.0001958896 0.0002975838 0.0002975838


[[12]][[3]]


[[13]]
[[13]][[1]]
$sites
               PCoA1        PCoA2
FRP_1_1 -0.043527957  0.011351539
FRP_1_2 -0.032976299  0.014708119
FRP_1_3 -0.032850891  0.001208606
FRP_2_1  0.009039313 -0.014005872
FRP_2_2  0.013318968 -0.034057330
FRP_2_3 -0.001794822 -0.024393836
FRP_3_1  0.032644030  0.014757002
FRP_3_2  0.020260421  0.001188887
FRP_3_3  0.035887237  0.029242885

$centroids
         PCoA1        PCoA2
1 -0.036081254  0.009781335
2  0.007090076 -0.024054386
3  0.030458935  0.015311410

attr(,"class")
[1] "ordiplot"

[[13]][[2]]
[[13]][[2]]$x
 [1] 0.06086860 0.07114383 0.08012382 0.08518861 0.07275641 0.08856228 0.08928266 0.09328219
 [9] 0.06172993 0.07211751 0.07842937 0.07533160 0.07930017 0.07778332 0.08782613 0.07285193
[17] 0.07791684 0.07531576 0.08535958 0.08282685 0.08922099 0.06112444 0.06361369 0.06988273
[25] 0.06881333 0.06859137 0.06440816 0.06434279 0.07332768 0.07979237 0.07490397 0.07404759
[33] 0.07700380 0.06288039 0.05916057 0.07839304

[[13]][[2]]$y
 [1] 0.02155168 0.03313613 0.04622126 0.05364545 0.04305751 0.07181291 0.06838762 0.08689952
 [9] 0.01306518 0.03334643 0.04365034 0.03982185 0.05506183 0.04801925 0.07268174 0.03822165
[17] 0.04948345 0.04942874 0.05441125 0.04248422 0.07374824 0.01138114 0.01965011 0.02695631
[25] 0.03347372 0.04071185 0.01652550 0.02688061 0.03996718 0.03474027 0.04308584 0.05306216
[33] 0.05048494 0.02141496 0.02035557 0.04079212

[[13]][[2]]$yf
 [1] 0.01658839 0.03352489 0.04587202 0.05364545 0.04041545 0.07165763 0.07165763 0.08689952
 [9] 0.01658839 0.03352489 0.04587202 0.04587202 0.04587202 0.04587202 0.07165763 0.04041545
[17] 0.04587202 0.04587202 0.05441125 0.04587202 0.07165763 0.01658839 0.02053253 0.03352489
[25] 0.03352489 0.03352489 0.02170305 0.02170305 0.04041545 0.04587202 0.04587202 0.04587202
[33] 0.04587202 0.02053253 0.01658839 0.04587202


[[13]][[3]]


[[14]]
[[14]][[1]]
$sites
               PCoA1        PCoA2
FRP_1_1  0.042477249  0.015020540
FRP_1_3 -0.008705514 -0.011308060
FRP_2_2 -0.039941823  0.016374830
FRP_2_3 -0.011523275  0.017305029
FRP_1_2 -0.026860165 -0.009229914
FRP_2_1 -0.013414343  0.007431860
FRP_3_1  0.036776069 -0.011054285
FRP_3_2  0.030465606  0.003716913
FRP_3_3 -0.009273805 -0.028256913

$centroids
         PCoA1        PCoA2
1 -0.007614499 -0.008680049
2 -0.017098149  0.013273441
3  0.025892144 -0.007623800

attr(,"class")
[1] "ordiplot"

[[14]][[2]]
[[14]][[2]]$x
 [1] 0.06201681 0.08379503 0.06281956 0.07596679 0.06238529 0.03570633 0.03218882 0.06980164
 [9] 0.04985751 0.03545178 0.03152288 0.03054132 0.04921456 0.05136571 0.03170078 0.04338915
[17] 0.03339715 0.04452646 0.08425700 0.07508478 0.05790462 0.04206506 0.02490822 0.06007275
[25] 0.04980744 0.05155351 0.03934190 0.06633336 0.06273824 0.03642909 0.05948173 0.05516107
[33] 0.04257256 0.03229863 0.05593606 0.05422477

[[14]][[2]]$y
 [1] 0.08990354 0.14220277 0.08858341 0.11813694 0.08863302 0.04286692 0.02773494 0.09743203
 [9] 0.06572478 0.04139409 0.02909842 0.02940850 0.07108072 0.06292971 0.02647966 0.05512236
[17] 0.04404198 0.05363331 0.13458374 0.11943448 0.08519786 0.05021637 0.01214949 0.08977383
[25] 0.06888602 0.06776615 0.03983410 0.09974328 0.09164974 0.04183135 0.08406625 0.06613679
[33] 0.05586437 0.02784326 0.06714696 0.06981000

[[14]][[2]]$yf
 [1] 0.08943680 0.13839325 0.09011657 0.11878571 0.08943680 0.04199369 0.02811296 0.09858765
 [9] 0.06715531 0.04199369 0.02811296 0.02811296 0.06715531 0.06715531 0.02811296 0.05487335
[17] 0.04199369 0.05487335 0.13839325 0.11878571 0.08463206 0.05021637 0.01214949 0.08943680
[25] 0.06715531 0.06771498 0.04199369 0.09858765 0.09011657 0.04199369 0.08463206 0.06771498
[33] 0.05487335 0.02811296 0.06771498 0.06771498


[[14]][[3]]


[[15]]
[[15]][[1]]
$sites
              PCoA1         PCoA2
FRP_1_1 -0.02427211 -0.0123948579
FRP_1_2 -0.05952153 -0.0036819105
FRP_1_3 -0.04205212 -0.0009833522
FRP_2_1  0.04098276 -0.0305990743
FRP_2_2  0.03478885 -0.0025208098
FRP_2_3  0.02023145 -0.0482700382
FRP_3_1 -0.01991263  0.0129203252
FRP_3_2  0.02214500  0.0635419330
FRP_3_3  0.02761034  0.0219877847

$centroids
         PCoA1        PCoA2
1 -0.042307299 -0.005225575
2  0.033279852 -0.028778735
3  0.009168741  0.031815020

attr(,"class")
[1] "ordiplot"

[[15]][[2]]
[[15]][[2]]$x
 [1] 0.08576216 0.07848963 0.09069074 0.09735769 0.09433685 0.07656739 0.11131934 0.09813733
 [9] 0.07561098 0.11808273 0.10886986 0.11029262 0.08818096 0.12274594 0.11237549 0.10493664
[17] 0.10751488 0.09186722 0.07988331 0.10434985 0.10612594 0.07901764 0.07086133 0.09598824
[25] 0.10700354 0.09731846 0.09275430 0.08834169 0.10481250 0.08424695 0.10365324 0.11738720
[33] 0.10538697 0.10073238 0.09728598 0.10315301

[[15]][[2]]$y
 [1] 0.04626202 0.02188582 0.05388583 0.06114058 0.04519068 0.02380277 0.08502777 0.06924693
 [9] 0.02568341 0.10012724 0.10472714 0.08858142 0.05303957 0.09966930 0.10621787 0.07509350
[17] 0.07907252 0.06662529 0.02884220 0.08385014 0.08186960 0.02452720 0.02786909 0.05693966
[25] 0.09542051 0.04915980 0.05084647 0.05393498 0.07595920 0.02547630 0.05960363 0.11171781
[33] 0.07301046 0.06123406 0.05324179 0.05313534

[[15]][[2]]$yf
 [1] 0.04626202 0.02475366 0.05372806 0.06087211 0.05372806 0.02475366 0.09277878 0.06087211
 [9] 0.02475366 0.10443306 0.09277878 0.09277878 0.05303957 0.10443306 0.10443306 0.07697833
[17] 0.08724652 0.05372806 0.02715925 0.07697833 0.08186960 0.02475366 0.02475366 0.05372806
[25] 0.08724652 0.05372806 0.05372806 0.05372806 0.07697833 0.02715925 0.06087211 0.10443306
[33] 0.07697833 0.06087211 0.05372806 0.06087211


[[15]][[3]]


[[16]]
[[16]][[1]]
$sites
             PCoA1         PCoA2
FRP_1_1  0.3537841 -0.1528578459
FRP_1_2 -0.4443153  0.0038387297
FRP_1_3 -0.4620054 -0.0026233080
FRP_2_1 -0.4328660 -0.0008743293
FRP_2_2 -0.4429111 -0.0059762581
FRP_3_1  0.3366731  0.2130984153
FRP_2_3  0.3603054  0.0351586086
FRP_3_2  0.3653875  0.0295101847
FRP_3_3  0.3659477 -0.1192741969

$centroids
       PCoA1        PCoA2
1 -0.4251988 -0.003579352
2 -0.4109515 -0.001837623
3  0.3632207  0.031135354

attr(,"class")
[1] "ordiplot"

[[16]][[2]]
[[16]][[2]]$x
 [1] 0.82055054 0.83524384 0.80963083 0.81654136 0.37353272 0.21846695 0.21134329 0.15508352
 [9] 0.09821099 0.14626130 0.12856256 0.81383733 0.81211133 0.81642131 0.82697879 0.15288017
[17] 0.13835122 0.83208145 0.83173512 0.83454197 0.84523568 0.09353650 0.80507034 0.80143742
[25] 0.80639881 0.81636467 0.81493099 0.80900714 0.81565615 0.82518040 0.21387904 0.21772577
[33] 0.34441298 0.09457567 0.20328760 0.20024089

[[16]][[2]]$y
 [1] 39.2915766 39.3791839 39.2179481 39.2861754  0.6490365  0.3772715  0.2755395  0.1491393
 [9]  0.1064509  0.1079765  0.1103326 39.2248480 39.2227116 39.2312678 39.3088011  0.1623112
[17]  0.1055282 39.3114502 39.3097443 39.3184595 39.3961801  0.0749907 39.1499296 39.1483395
[25] 39.1570998 39.2348751 39.2176355 39.2162696 39.2251123 39.3029845  0.2744268  0.3765315
[33]  0.5047577  0.1023846  0.2390443  0.1436170

[[16]][[2]]$yf
 [1] 39.2915766 39.3791839 39.2179481 39.2861754  0.6490365  0.3772715  0.2749832  0.1516892
 [9]  0.1064509  0.1079765  0.1079304 39.2217317 39.2217317 39.2330715 39.3088011  0.1516892
[17]  0.1079304 39.3114502 39.3097443 39.3184595 39.3961801  0.0749907 39.1499296 39.1483395
[25] 39.1570998 39.2330715 39.2217317 39.2162696 39.2251123 39.3029845  0.2749832  0.3765315
[33]  0.5047577  0.1023846  0.2390443  0.1516892


[[16]][[3]]


[[17]]
[[17]][[1]]
$sites
              PCoA1        PCoA2
FRP_1_1  0.01505350  0.059210002
FRP_1_2 -0.14227806  0.034091476
FRP_1_3 -0.03570188  0.031360964
FRP_2_1 -0.10430802 -0.076744177
FRP_2_2 -0.01705433 -0.015335733
FRP_2_3  0.06658511 -0.027376966
FRP_3_1  0.02747575  0.004856602
FRP_3_2  0.07465431  0.008196539
FRP_3_3  0.11557362 -0.018258706

$centroids
        PCoA1       PCoA2
1 -0.04252509  0.03644253
2 -0.01705410 -0.01533649
3  0.07468497  0.00368404

attr(,"class")
[1] "ordiplot"

[[17]][[2]]
[[17]][[2]]$x
 [1] 0.18814418 0.11438183 0.18502201 0.10144671 0.12301930 0.10484946 0.10878281 0.15711948
 [9] 0.13088348 0.13778015 0.14409683 0.22414664 0.18792174 0.22193397 0.26576886 0.14486299
[17] 0.09535830 0.13315769 0.11827795 0.13696799 0.16652484 0.11787937 0.18569309 0.16777719
[25] 0.20374792 0.23559904 0.09527475 0.08841202 0.10733487 0.14268961 0.10219088 0.06237581
[33] 0.07476404 0.08104493 0.11845778 0.07111603

[[17]][[2]]$y
 [1] 0.32166028 0.20379367 0.21921166 0.10742709 0.17041519 0.07675010 0.16561887 0.24825111
 [9] 0.17453601 0.17644695 0.23717968 0.33935636 0.32163028 0.34845061 0.41919444 0.21353843
[17] 0.09661040 0.16612805 0.17040536 0.17605931 0.24477440 0.19578588 0.32469775 0.26058738
[25] 0.32808305 0.41524470 0.12891696 0.08445182 0.13272349 0.21945912 0.09377060 0.01266765
[33] 0.09055491 0.08893709 0.17341868 0.08844490

[[17]][[2]]$yf
 [1] 0.32266277 0.17855435 0.24320614 0.10069503 0.17855435 0.10069503 0.16561887 0.24320614
 [9] 0.17855435 0.17855435 0.22535906 0.34390349 0.32266277 0.34390349 0.41919444 0.22535906
[17] 0.10069503 0.17855435 0.17855435 0.17855435 0.24320614 0.17855435 0.32266277 0.24320614
[25] 0.32808305 0.41524470 0.10069503 0.08809718 0.13272349 0.21945912 0.10069503 0.01266765
[33] 0.08809718 0.08809718 0.17855435 0.08809718


[[17]][[3]]


[[18]]
[[18]][[1]]
$sites
                PCoA1        PCoA2
FRP_3_1 -0.0490158741  0.069974338
FRP_1_1  0.0521405986 -0.060010873
FRP_1_2 -0.0122007868 -0.035863827
FRP_1_3 -0.0560814854 -0.034592986
FRP_2_1 -0.0568673299 -0.014668197
FRP_2_2  0.0561813829  0.014198967
FRP_2_3  0.0028723044  0.024611023
FRP_3_2  0.0622897858  0.039831333
FRP_3_3  0.0006814045 -0.003479778

$centroids
         PCoA1       PCoA2
1 -0.010395929 -0.03975558
2  0.002767008  0.01777500
3  0.003578001  0.03029848

attr(,"class")
[1] "ordiplot"

[[18]][[2]]
[[18]][[2]]$x
 [1] 0.16739987 0.11917032 0.11625996 0.11369424 0.12820644 0.09682311 0.13001944 0.10868329
 [9] 0.09029646 0.12197112 0.12978463 0.09424100 0.11285375 0.10944510 0.10610557 0.07630210
[17] 0.09860753 0.09716000 0.08778651 0.12829344 0.05962381 0.05959634 0.12454594 0.10430116
[25] 0.14563682 0.09613411 0.13637689 0.09142042 0.13263442 0.10964158 0.08359524 0.06121327
[33] 0.08474156 0.08451367 0.07169515 0.11097404

[[18]][[2]]$y
 [1] 0.22259161 0.16465759 0.13529431 0.11563195 0.16716543 0.09726252 0.17282349 0.12610815
 [9] 0.08687813 0.16440795 0.18863612 0.09071839 0.12589840 0.12425316 0.09886762 0.07763543
[17] 0.10326807 0.11603056 0.08567575 0.15132178 0.04905376 0.03033327 0.17286705 0.10750747
[25] 0.20228211 0.08970617 0.18491389 0.11168856 0.21072371 0.10363278 0.07920898 0.03681810
[33] 0.08316150 0.09974460 0.03704095 0.11374954

[[18]][[2]]$yf
 [1] 0.22259161 0.16408396 0.13529431 0.12076517 0.16408396 0.09734391 0.18072980 0.11693591
 [9] 0.08886499 0.16408396 0.18072980 0.09734391 0.12076517 0.11693591 0.10641843 0.07763543
[17] 0.10641843 0.10641843 0.08886499 0.16408396 0.04097094 0.03033327 0.16408396 0.10641843
[25] 0.20228211 0.09734391 0.19781880 0.09734391 0.19781880 0.11693591 0.07920898 0.04097094
[33] 0.08886499 0.08886499 0.04097094 0.11693591


[[18]][[3]]


[[19]]
[[19]][[1]]
$sites
               PCoA1         PCoA2
FRP_3_3  0.052988568 -0.0259271679
FRP_3_1 -0.015116037 -0.0022369354
FRP_1_2 -0.036629255 -0.0165120432
FRP_2_1 -0.002453918  0.0166542737
FRP_2_2  0.043587689  0.0138208375
FRP_3_2 -0.047366837 -0.0158009814
FRP_2_3 -0.015422339  0.0149112453
FRP_1_1  0.040909376 -0.0005781309
FRP_1_3 -0.020497246  0.0156689021

$centroids
         PCoA1        PCoA2
1 -0.017081124  0.006947953
2 -0.001453417  0.016302932
3 -0.015116035 -0.002236940

attr(,"class")
[1] "ordiplot"

[[19]][[2]]
[[19]][[2]]$x
 [1] 0.07375933 0.08979282 0.07134401 0.04395821 0.10307922 0.08082303 0.03377940 0.08549538
 [9] 0.03037829 0.02853798 0.06188963 0.03886611 0.02814638 0.05983715 0.02798022 0.05004521
[17] 0.08819445 0.02742754 0.04148364 0.08232858 0.03693615 0.05173173 0.05962204 0.02551725
[25] 0.05070838 0.02962020 0.09689496 0.06533620 0.03006977 0.06563447 0.05142715 0.09097873
[33] 0.04656276 0.06133175 0.02365013 0.06691140

[[19]][[2]]$y
 [1] 0.12279749 0.15575899 0.11379533 0.07248771 0.17822877 0.13609601 0.05102552 0.13737189
 [9] 0.05102003 0.02407788 0.09751311 0.06625453 0.02408037 0.09160554 0.02408649 0.07504973
[17] 0.14603746 0.02328230 0.06624393 0.13608841 0.06510986 0.07673530 0.08905878 0.02269272
[25] 0.07503954 0.02413835 0.16363236 0.09746251 0.02412996 0.09909466 0.07503812 0.15575915
[33] 0.07359151 0.09751677 0.00165095 0.09903919

[[19]][[2]]$yf
 [1] 0.12279749 0.15575899 0.11379533 0.07248771 0.17822877 0.13609221 0.05102552 0.13737189
 [9] 0.05102003 0.02408158 0.09749746 0.06624923 0.02408158 0.09160554 0.02408158 0.07504247
[17] 0.14603746 0.02328230 0.06624923 0.13609221 0.06510986 0.07673530 0.08905878 0.02269272
[25] 0.07504247 0.02413416 0.16363236 0.09749746 0.02413416 0.09906693 0.07504247 0.15575915
[33] 0.07359151 0.09749746 0.00165095 0.09906693


[[19]][[3]]

Vouchered Reference dataframe

Here, I’m using a loc list that doesn’t include 16Sfish and crust2 because they have too few samples to include.

apparently, also 18SSSU3

loc_list18
 [1] "16SH1"        "16Svar"       "18Sn4"        "L2513H2714"   "aquaF2"       "aquaF3"      
 [7] "cep"          "ceph16S"      "crust16S"     "fishcoilbc"   "fishminiA"    "mifish"      
[13] "minibar"      "nsCOIFo"      "plankCOI"     "shark474"     "sharkCOImini" "short28S"    
# cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
# two of the loci were removed because they had too little data remaining after the sodm filter step
lapply(loc_list19, simple.bray, sodm_filtered_df = vrp_sodm_filtered_df, sample = "VRP")
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'seq'. You can override using the `.groups` argument.
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]

[[8]]

[[9]]

[[10]]

[[11]]

[[12]]

[[13]]

[[14]]

[[15]]

[[16]]

[[17]]

[[18]]

[[19]]
NA

Remove dissimilar replicates

Based on the output from 04-filter-ASV-by-SODM.Rmd, I now want to filter the dataset to remove replicates for loci that have high dissimilarity values, listed here: extdata/downsampled_loci/data/samples_to_remove_downsampled.csv.

# bind together the vouchered and full reference SODM dataframes
reference_df_sodm_filtered <- vrp_sodm_filtered_df %>% bind_rows(frp_sodm_filtered_df)

A clean, non-redundant version of the reference sample dataframe

So that is the dataframe from which we want to remove this particular list of locus-samples

# read in the list of samples to remove
tossers <- read_csv("../extdata/downsampled_loci/data/samples_to_remove_downsampled.csv")
Parsed with column specification:
cols(
  locus = col_character(),
  sample = col_character()
)

It turns out that an anti-join is all I need for this filtering step.

ref_sodm_bray_filtered_unique <- ref_sodm_filtered_unique %>%
  anti_join(., tossers, by = c("locus", "sample")) 

Okay, so that is the dataset that I can work through the assessment analyses with, beginning with summary statistics, then adding in the taxonomy and assessing true/false positives in the vouchered samples and breadth of taxonomic coverage in the full reference pool.

Save filtered dataframe

Save the filtered feature table output from occupancy modeling and dissimilarity

# save this version of the feature table to combine with taxonomy for locus-integrated taxonomy
ref_sodm_bray_filtered_unique %>%
  saveRDS("../extdata/downsampled_loci/data/feature_table_sodm_bray_filtered.rds", compress = "xz")
---
title: "Filtering ASVs based on dissimilarity across sample replicates"
output: html_notebook
---
20 May 2021

Here we'll use the Bray-Curtis index to identify sample replicates (within each locus) that are more dissimilar than similar to the other replicates. Dissimilarity can be an indication of an issue (contamination, etc.) with a particular replicate.

This uses the output from the species-occupancy detection modeling in `03-species-occupancy-model.Rmd`
The occupancy modeling uses the ASVs (not taxonomy) and I'll use a similar approach here with the Bray-Curtis and NMDS analyses.


## Process

The process is a bit cumbersome because it requires looking at each sample/locus/replicate for the full reference DNA pool and vouchered reference pool.

The process for looking at the dissimilarity among replicates is to:
1. Read in data that has been cleaned up using the occupancy modeling
2. Create a community matrix (per locus)
3. Standardize data across replicates (fct decostand)
4. Generate Bray-Curtis distances (fct vegdist)
   4a. Are any replicates more dissimilar than similar? 
5. Generate NMDS plots from distance matrix (fct metaMDS)

6. Read in list



## Outputs

Based on the NMDS plots and Bray-Curtis dissimilarity index, I generated a list of samples to remove:

`../data/reference_pool_dissimilarity_samples_to_remove.csv`

The data that were used to generate that list are output .csv files from the Bray-Curtis function, implemented below.

In addition, three loci had insufficient data across all 18 samples in both the vouchered and full reference pools to be included in these analyses and will be dropped from further analyses:

16Sfish
teleo
crust2


```{r load-functions}
source("../R/metabarcoding-funcs.R")
```



```{r load-libs-and-data}
library(tidyverse)
library(stringi)
library(vegan)
library(reshape2)
library(textshape)
library(rlist)


# output from the ASV filtering based on the SODM for 
# vouchered ref
vrp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/voucher_features_sodm_filtered_taxonomy_df.rds")

# full reference
frp_sodm_filtered_df <- readRDS("../extdata/downsampled_loci/data/full_reference_sodm_filtered_taxonomy_df.rds")

```


I have wrapped the Bray-Curis and NMDS up into a function called `bray_nmds_complete` which outputs a .csv file with the replicates that are > 0.49 dissimilar and generates an NMDS plot.


I'll use that function with an lappy and the list of loci, since each locus will be analyzed separately. 

To cycle over a list of the loci...
```{r}
# grab the names of the loci from the full dataframe
locs <- frp_sodm_filtered_df %>%
  select(locus) %>%
  unique() %>%
  as.list()

# turn that into a list that could be cycled over
loc_list <- locs$locus

# make a separate list for the VRP samples
locs19 <- frp_sodm_filtered_df %>%
  select(locus) %>%
  unique() %>%
  filter(!locus %in% c("crust2", "16Sfish", "teleo")) %>%
  as.list()

# turn that into a list that could be cycled over
loc_list19 <- locs19$locus

```



## Full Reference dataframe

```{r apply-bray-fct-over-full-reference-pool}
# cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
lapply(loc_list19, bray_nmds_complete, sodm_filtered_df = frp_sodm_filtered_df, sample = "FRP")
```

## Vouchered Reference dataframe

Here, I'm using a loc list that doesn't include 16Sfish and crust2 because they have too few samples to include. 

apparently, also 18SSSU3

```{r}
# make a separate list for the VRP samples
locs18 <- frp_sodm_filtered_df %>%
  select(locus) %>%
  unique() %>%
  filter(!locus %in% c("crust2", "16Sfish", "teleo", "18SSSU3")) %>%
  as.list()

# turn that into a list that could be cycled over
loc_list18 <- locs18$locus

```


```{r apply-bray-filter-vouchered-samples}
# cycle over the list of loci for the full reference pool sample replicates
# using the bray-curtis function to test for dissimilarity
# two of the loci were removed because they had too little data remaining after the sodm filter step
lapply(loc_list19, simple.bray, sodm_filtered_df = vrp_sodm_filtered_df, sample = "VRP")

```



## Remove dissimilar replicates

Based on the output from `04-filter-ASV-by-SODM.Rmd`, I now want to filter the dataset to remove replicates for loci that have high dissimilarity values, listed here: `extdata/downsampled_loci/data/samples_to_remove_downsampled.csv`.


```{r bind-reference-dataframes}
# bind together the vouchered and full reference SODM dataframes
reference_df_sodm_filtered <- vrp_sodm_filtered_df %>% bind_rows(frp_sodm_filtered_df)

```


A clean, non-redundant version of the reference sample dataframe
```{r}
ref_sodm_filtered_unique <- reference_df_sodm_filtered %>% 
  select(locus, seq, sample, count) %>%
  unique() %>% # if there are multiple entries with different counts, we want to collapse those reads
  group_by(locus, seq, sample) %>%
  mutate(total_reads = sum(count)) %>%
  select(-count) %>%
  rename(count = total_reads)

```

So that is the dataframe from which we want to remove this particular list of locus-samples
```{r dissimilar-samples-to-remove}
# read in the list of samples to remove
tossers <- read_csv("../extdata/downsampled_loci/data/samples_to_remove_downsampled.csv")

```

It turns out that an anti-join is all I need for this filtering step.
```{r}
ref_sodm_bray_filtered_unique <- ref_sodm_filtered_unique %>%
  anti_join(., tossers, by = c("locus", "sample")) 

```

Okay, so that is the dataset that I can work through the assessment analyses with, beginning with summary statistics, then adding in the taxonomy and assessing true/false positives in the vouchered samples and breadth of taxonomic coverage in the full reference pool.


## Save filtered dataframe

Save the filtered feature table output from occupancy modeling and dissimilarity
```{r}
# save this version of the feature table to combine with taxonomy for locus-integrated taxonomy
ref_sodm_bray_filtered_unique %>%
  saveRDS("../extdata/downsampled_loci/data/feature_table_sodm_bray_filtered.rds", compress = "xz")

```
